Project 5 Group 4¶
- Xiaotong Li (xl2788)
- Runzi Qiang (rq2156)
Introduction¶
This jupyter notebook shows the process we build three models of identifing and classifing toxic online comments, which predicts a probability of each type of toxicity for each comment.
Models:¶
Baseline: Naive Bayes
LTSM Model
LTSM 6Layers Model (used on our website)
Import packages¶
In [64]:
# Package Imports
import re
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
In [65]:
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score
from sklearn.multiclass import OneVsRestClassifier
from nltk.corpus import stopwords
from sklearn.svm import LinearSVC
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
import seaborn as sns
import nltk
nltk.download('stopwords')
stop_words = set(stopwords.words('english'))
from tqdm import tqdm_notebook
from nltk.corpus import stopwords
from sklearn.model_selection import train_test_split
from tensorflow.keras import regularizers, initializers, optimizers, callbacks
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
from keras.utils.np_utils import to_categorical
from tensorflow.keras.layers import *
from tensorflow.keras.models import Model
[nltk_data] Downloading package stopwords to /Users/runzi/nltk_data... [nltk_data] Package stopwords is already up-to-date!
The Data¶
The dataset we used are originally from kaggle(https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data).
Please download the data and change data path.
In [66]:
# Data Path
# you can change data path
dataPath = "../data"
train = pd.read_csv(dataPath + "/train.csv")
test = pd.read_csv(dataPath + "/test.csv")
In [10]:
MAX_NB_WORDS = 100000 # max no. of words for tokenizer
MAX_SEQUENCE_LENGTH = 200 # max length of each entry (sentence), including padding
VALIDATION_SPLIT = 0.2 # data for validation (not used in training)
EMBEDDING_DIM = 100 # embedding dimensions for word vectors (word2vec/GloVe)
GLOVE_DIR = "../data/glove/glove.6B."+str(EMBEDDING_DIM)+"d.txt" #you can change glove path
In [11]:
df_toxic = train.drop(['id', 'comment_text'], axis=1)
counts = []
categories = list(df_toxic.columns.values)
for i in categories:
counts.append((i, df_toxic[i].sum()))
df_stats = pd.DataFrame(counts, columns=['category', 'number_of_comments'])
df_stats
Out[11]:
| category | number_of_comments | |
|---|---|---|
| 0 | toxic | 15294 |
| 1 | severe_toxic | 1595 |
| 2 | obscene | 8449 |
| 3 | threat | 478 |
| 4 | insult | 7877 |
| 5 | identity_hate | 1405 |
In [12]:
#comments have multi labels
df_stats.plot(x='category', y='number_of_comments', kind='bar', legend=False, grid=True, figsize=(8, 5))
plt.title("Number of comments per category")
plt.ylabel('# of Occurrences', fontsize=12)
plt.xlabel('category', fontsize=12)
Out[12]:
Text(0.5, 0, 'category')
In [13]:
rowsums = train.iloc[:,2:].sum(axis=1)
x=rowsums.value_counts()
#plot
plt.figure(figsize=(8,5))
ax = sns.barplot(x.index, x.values)
plt.title("Multiple categories per comment")
plt.ylabel('# of Occurrences', fontsize=12)
plt.xlabel('# of categories', fontsize=12)
Out[13]:
Text(0.5, 0, '# of categories')
In [14]:
#The distribution of the number of words in comment texts.
lens = train.comment_text.str.len()
lens.hist(bins = np.arange(0,5000,50))
Out[14]:
<matplotlib.axes._subplots.AxesSubplot at 0x1367010b8>
In [67]:
print('Number of missing comments in comment text:')
train['comment_text'].isnull().sum()
Number of missing comments in comment text:
Out[67]:
0
Naive Bayes¶
Data preprocessing: remove stopwords, punctuation and make text lowercase.
In [69]:
def clean(text):
text = text.lower()
text = re.sub(r"what's", "what is ", text)
text = re.sub(r"\'s", " ", text)
text = re.sub(r"\'ve", " have ", text)
text = re.sub(r"can't", "can not ", text)
text = re.sub(r"n't", " not ", text)
text = re.sub(r"i'm", "i am ", text)
text = re.sub(r"\'re", " are ", text)
text = re.sub(r"\'d", " would ", text)
text = re.sub(r"\'ll", " will ", text)
text = re.sub(r"\'scuse", " excuse ", text)
text = re.sub('\W', ' ', text)
text = re.sub('\s+', ' ', text)
text = text.strip(' ')
return text
train['comment_text'] = train['comment_text'].map(lambda com : clean(com))
train['comment_text']
categories = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
train, test = train_test_split(train, random_state=42, test_size=0.33, shuffle=True)
X_train = train.comment_text
X_test = test.comment_text
print(X_train.shape)
print(X_test.shape)
# Define a pipeline combining a text feature extractor with multi lable classifier
from sklearn.externals import joblib
import pickle
NB_pipeline = Pipeline([
('tfidf', TfidfVectorizer(stop_words=stop_words)),
('clf', OneVsRestClassifier(MultinomialNB(
fit_prior=True, class_prior=None))),
])
for category in categories:
print('... Processing {}'.format(category))
# train the model using X_dtm & y
NB_pipeline.fit(X_train, train[category])
# save model
joblib.dump(NB_pipeline, category+'_model.pkl')
# compute the testing accuracy
prediction = NB_pipeline.predict(X_test)
print('Test accuracy is {}'.format(accuracy_score(test[category], prediction)))
(71631,) (35281,) ... Processing toxic Test accuracy is 0.9204671069414132 ... Processing severe_toxic Test accuracy is 0.9897395198548794 ... Processing obscene Test accuracy is 0.95195714407188 ... Processing threat Test accuracy is 0.9972223009551884 ... Processing insult Test accuracy is 0.9524106459567473 ... Processing identity_hate Test accuracy is 0.9911567132450895
In [85]:
#test
comments = "Really stupid in this place. fuck off you troll!"
comments = pd.Series(comments)
#comments_1 = preprocessing_test(comments, tokenizer)
comments1 = comments.map(lambda com : clean(com))
comments1
Out[85]:
0 really stupid in this place fuck off you troll dtype: object
In [84]:
print('Test Accuracy:')
for category in categories:
# Load from file
joblib_model = joblib.load(category+'_model.pkl')
# Calculate the accuracy and predictions
#score = joblib_model.score(Xtest, Ytest)
#print("Test score: {0:.2f} %".format(100 * score))
Ypredict = joblib_model.predict(comments1)
print(category, Ypredict)
Test Accuracy: toxic [1] severe_toxic [0] obscene [0] threat [0] insult [0] identity_hate [0]
LTMS Model¶
In [93]:
dataPath = "../data"
train = pd.read_csv(dataPath + "/train.csv")
test = pd.read_csv(dataPath + "/test.csv")
train.isnull().sum()
Out[93]:
id 0 comment_text 0 toxic 0 severe_toxic 0 obscene 0 threat 0 insult 0 identity_hate 0 dtype: int64
Data Pre-processing¶
We create a tokenizer, configured to only take into account 100,000 most common words. Then we build the word index and can recover the word index that was computed, this is also the mapping of how the words are converted to sequence of integers.
Since the sequence may not be of the same length, we are also going to pad the sequence by adding zeros to match the length.
We will save this tokenizer because we are going to apply the same tokenizer to the test data. Thus we can keep the same corpus.
In [18]:
def clean_text(text, remove_stopwords = True):
output = ""
text = str(text).replace("\n", "")
text = re.sub(r'[^\w\s]','',text).lower()
if remove_stopwords:
text = text.split(" ")
for word in text:
if word not in stopwords.words("english"):
output = output + " " + word
else:
output = text
return str(output.strip())[1:-3].replace(" ", " ")
def preprocessing_train(test):
MAX_NB_WORDS = 100000 # max no. of words for tokenizer
MAX_SEQUENCE_LENGTH = 200 # max length of each entry (sentence), including padding
VALIDATION_SPLIT = 0.2 # data for validation (not used in training)
EMBEDDING_DIM = 100 # embedding dimensions for word vectors (word2vec/GloVe)
GLOVE_DIR = "../../Data/Toxic-Thread-Detector/glove/glove.6B."+str(EMBEDDING_DIM)+"d.txt"
texts = []
tokenizer = Tokenizer(num_words=MAX_NB_WORDS)
for line in tqdm_notebook(test, total=len(test)):
texts.append(clean_text(line))
tokenizer.fit_on_texts(texts)
sequences = tokenizer.texts_to_sequences(texts)
word_index = tokenizer.word_index
data = pad_sequences(sequences, padding = 'post', maxlen = MAX_SEQUENCE_LENGTH)
return data, tokenizer
def preprocessing_test(test, tokenizer):
MAX_NB_WORDS = 100000 # max no. of words for tokenizer
MAX_SEQUENCE_LENGTH = 200 # max length of each entry (sentence), including padding
VALIDATION_SPLIT = 0.2 # data for validation (not used in training)
EMBEDDING_DIM = 100 # embedding dimensions for word vectors (word2vec/GloVe)
GLOVE_DIR = "../../Data/Toxic-Thread-Detector/glove/glove.6B."+str(EMBEDDING_DIM)+"d.txt"
texts = []
for line in tqdm_notebook(test, total=len(test)):
texts.append(clean_text(line))
tokenizer.fit_on_texts(texts)
sequences = tokenizer.texts_to_sequences(texts)
word_index = tokenizer.word_index
data = pad_sequences(sequences, padding = 'post', maxlen = MAX_SEQUENCE_LENGTH)
return data
In [ ]:
X = train["comment_text"]
#It takes about 20 minutes to preprocess the data
data, tokenizer = preprocessing_train(X)
In [90]:
print(data)
[[ 7137 39 192 ... 0 0 0] [ 2602 896 518 ... 0 0 0] [18579 609 37993 ... 0 0 0] ... [ 2515 1198 1537 ... 0 0 0] [36366 35201 1096 ... 0 0 0] [ 649 305 243 ... 0 0 0]]
In [94]:
labels = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
y = train[labels].values
# random shuffle the entries
np.random.seed(1)
indices = np.arange(data.shape[0])
np.random.shuffle(indices)
data = data[indices]
labels = y[indices]
#Create the train-validation split.
num_validation_samples = int(VALIDATION_SPLIT*data.shape[0])
x_train = data[: -num_validation_samples]
y_train = labels[: -num_validation_samples]
x_val = data[-num_validation_samples: ]
y_val = labels[-num_validation_samples: ]
print('Number of entries in each category:')
print('training: ', y_train.sum(axis=0))
print('validation: ', y_val.sum(axis=0))
print('Tokenized sentences: \n', data[10])
print('One hot label: \n', labels[10])
Number of entries in each category:
training: [12331 1299 6787 381 6361 1130]
validation: [2963 296 1662 97 1516 275]
Tokenized sentences:
[ 2233 480 20 35 23 96 1613 60 301 11488 44 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0]
One hot label:
[0 0 0 0 0 0]
Train LSTM model¶
In [95]:
word_index = tokenizer.word_index
embeddings_index = {}
f = open(GLOVE_DIR,encoding="utf8")
print('Loading GloVe from:', GLOVE_DIR,'...', end='')
for line in f:
values = line.split()
word = values[0]
embeddings_index[word] = np.asarray(values[1:], dtype='float32')
f.close()
print("Done.\n Proceeding with Embedding Matrix...", end="")
embedding_matrix = np.random.random((len(word_index) + 1, EMBEDDING_DIM))
for word, i in word_index.items():
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None:
embedding_matrix[i] = embedding_vector
print(" Completed!")
Loading GloVe from: ../data/glove/glove.6B.100d.txt ...Done. Proceeding with Embedding Matrix... Completed!
Architecture: the architecture of this model is
- The input sequence is of length 200. Which is also construting our input layer.
- The embedding layer is like extracting the features from the data. The embedding matrix is constructed with the help of GloVe, which is a pretrained weights downloaded from the standford nlp labs. After the output from this layer will shrinks to 100.
- And then it is met by a maxpool layer.
- We apply the dropout layer to ensure not to overfit the model.
- We then do a relu dense network to further shinks down the dimension.
- The classifier layer is 6 to match the expected output.
In [96]:
sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')
embedding_layer = Embedding(len(word_index) + 1,
EMBEDDING_DIM,
weights = [embedding_matrix],
input_length = MAX_SEQUENCE_LENGTH,
trainable=False,
name = 'embeddings')
embedded_sequences = embedding_layer(sequence_input)
x = LSTM(60, return_sequences=True,name='lstm_layer')(embedded_sequences)
x = GlobalMaxPool1D()(x)
x = Dropout(0.1)(x)
x = Dense(50, activation="relu")(x)
x = Dropout(0.1)(x)
preds = Dense(6, activation="softmax")(x)
model0 = Model(sequence_input, preds)
model0.compile(loss = 'binary_crossentropy',
optimizer='adam',
metrics = ['accuracy'])
model0.summary()
Model: "model_6" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_7 (InputLayer) [(None, 200)] 0 _________________________________________________________________ embeddings (Embedding) (None, 200, 100) 32467000 _________________________________________________________________ lstm_layer (LSTM) (None, 200, 60) 38640 _________________________________________________________________ global_max_pooling1d_5 (Glob (None, 60) 0 _________________________________________________________________ dropout_10 (Dropout) (None, 60) 0 _________________________________________________________________ dense_16 (Dense) (None, 50) 3050 _________________________________________________________________ dropout_11 (Dropout) (None, 50) 0 _________________________________________________________________ dense_17 (Dense) (None, 6) 306 ================================================================= Total params: 32,508,996 Trainable params: 41,996 Non-trainable params: 32,467,000 _________________________________________________________________
In [40]:
#Train this model takes about 40 minutes.
print('Training progress:')
history = model0.fit(x_train, y_train, epochs = 2, batch_size=32, validation_data=(x_val, y_val))
Training progress: Train on 127657 samples, validate on 31914 samples Epoch 1/2 127657/127657 [==============================] - 817s 6ms/sample - loss: 0.2317 - accuracy: 0.9631 - val_loss: 0.2299 - val_accuracy: 0.9644 Epoch 2/2 127657/127657 [==============================] - 795s 6ms/sample - loss: 0.2315 - accuracy: 0.9631 - val_loss: 0.2298 - val_accuracy: 0.9644
In [41]:
# plot loss
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(loss)+1)
plt.plot(epochs, loss, label='Training loss')
plt.plot(epochs, val_loss, label='Validation loss')
plt.title('Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show();
# plot accurancy
accuracy = history.history['accuracy']
val_accuracy = history.history['val_accuracy']
plt.plot(epochs, accuracy, label='Training accuracy')
plt.plot(epochs, val_accuracy, label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epochs')
plt.legend()
plt.show();
Test¶
In [42]:
comments = "Really stupid in this place. fuck off you troll!"
comments = pd.Series(comments)
comments_1 = preprocessing_test(comments, tokenizer)
prediction1 = model.predict(comments_1)
prediction1
Out[42]:
[array([[0.5383628]], dtype=float32), array([[0.42010295]], dtype=float32), array([[0.51511616]], dtype=float32), array([[0.4491531]], dtype=float32), array([[0.4798976]], dtype=float32), array([[0.69885904]], dtype=float32)]
LTSM-6Layers-Model¶
- This approach is very similar to the the one-vs-rest methods when dealing with multi-label classification problem.
- We basically performs a classification tasks four times when it comes to the final layer (dense) to get a expected binary outcome.
- This is the best model to solve this model and it is giving a very good result.
- We save the model file to use for deployment.
In [97]:
y = train[["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]]
X_train, X_test, y_train, y_test = train_test_split(data, y, test_size=0.20, random_state=42)
In [98]:
# First output
y1_train = y_train[["toxic"]].values
y1_test = y_test[["toxic"]].values
# Second output
y2_train = y_train[["severe_toxic"]].values
y2_test = y_test[["severe_toxic"]].values
# Third output
y3_train = y_train[["obscene"]].values
y3_test = y_test[["obscene"]].values
# Fourth output
y4_train = y_train[["threat"]].values
y4_test = y_test[["threat"]].values
# Fifth output
y5_train = y_train[["insult"]].values
y5_test = y_test[["insult"]].values
# Sixth output
y6_train = y_train[["identity_hate"]].values
y6_test = y_test[["identity_hate"]].values
In [100]:
#Using embedding_matrix that once calculated
vocab_size = len(word_index)+1
input_1 = Input(shape=(200,))
embedding_layer = Embedding(vocab_size, 100, weights=[embedding_matrix], trainable=False)(input_1)
LSTM_Layer1 = LSTM(128)(embedding_layer)
output1 = Dense(1, activation='sigmoid')(LSTM_Layer1)
output2 = Dense(1, activation='sigmoid')(LSTM_Layer1)
output3 = Dense(1, activation='sigmoid')(LSTM_Layer1)
output4 = Dense(1, activation='sigmoid')(LSTM_Layer1)
output5 = Dense(1, activation='sigmoid')(LSTM_Layer1)
output6 = Dense(1, activation='sigmoid')(LSTM_Layer1)
model = Model(inputs=input_1, outputs=[output1, output2, output3, output4, output5, output6])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['acc'])
model.summary()
Model: "model_8" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_9 (InputLayer) [(None, 200)] 0 __________________________________________________________________________________________________ embedding_2 (Embedding) (None, 200, 100) 32467000 input_9[0][0] __________________________________________________________________________________________________ lstm_2 (LSTM) (None, 128) 117248 embedding_2[0][0] __________________________________________________________________________________________________ dense_24 (Dense) (None, 1) 129 lstm_2[0][0] __________________________________________________________________________________________________ dense_25 (Dense) (None, 1) 129 lstm_2[0][0] __________________________________________________________________________________________________ dense_26 (Dense) (None, 1) 129 lstm_2[0][0] __________________________________________________________________________________________________ dense_27 (Dense) (None, 1) 129 lstm_2[0][0] __________________________________________________________________________________________________ dense_28 (Dense) (None, 1) 129 lstm_2[0][0] __________________________________________________________________________________________________ dense_29 (Dense) (None, 1) 129 lstm_2[0][0] ================================================================================================== Total params: 32,585,022 Trainable params: 118,022 Non-trainable params: 32,467,000 __________________________________________________________________________________________________
In [105]:
history = model.fit(x=X_train, y=[y1_train, y2_train, y3_train, y4_train, y5_train, y6_train], batch_size=32, epochs=5, verbose=1, validation_split=0.2)
Train on 102124 samples, validate on 25532 samples Epoch 1/5 864/102124 [..............................] - ETA: 16:16 - loss: 0.3293 - dense_18_loss: 0.0746 - dense_19_loss: 0.0059 - dense_20_loss: 0.0314 - dense_21_loss: 0.0050 - dense_22_loss: 0.0479 - dense_23_loss: 0.1645 - dense_18_acc: 1.0000 - dense_19_acc: 1.0000 - dense_20_acc: 1.0000 - dense_21_acc: 1.0000 - dense_22_acc: 1.0000 - dense_23_acc: 0.96 - ETA: 15:00 - loss: 0.3454 - dense_18_loss: 0.1384 - dense_19_loss: 0.0054 - dense_20_loss: 0.0673 - dense_21_loss: 0.0046 - dense_22_loss: 0.0431 - dense_23_loss: 0.0866 - dense_18_acc: 0.9688 - dense_19_acc: 1.0000 - dense_20_acc: 0.9844 - dense_21_acc: 1.0000 - dense_22_acc: 1.0000 - dense_23_acc: 0.98 - ETA: 14:47 - loss: 0.3873 - dense_18_loss: 0.1396 - dense_19_loss: 0.0049 - dense_20_loss: 0.0520 - dense_21_loss: 0.0041 - dense_22_loss: 0.0752 - dense_23_loss: 0.1115 - dense_18_acc: 0.9688 - dense_19_acc: 1.0000 - dense_20_acc: 0.9896 - dense_21_acc: 1.0000 - dense_22_acc: 0.9896 - dense_23_acc: 0.97 - ETA: 14:32 - loss: 0.4752 - dense_18_loss: 0.1700 - dense_19_loss: 0.0400 - dense_20_loss: 0.0656 - dense_21_loss: 0.0040 - dense_22_loss: 0.1098 - dense_23_loss: 0.0859 - dense_18_acc: 0.9531 - dense_19_acc: 0.9922 - dense_20_acc: 0.9844 - dense_21_acc: 1.0000 - dense_22_acc: 0.9766 - dense_23_acc: 0.98 - ETA: 14:30 - loss: 0.4754 - dense_18_loss: 0.1700 - dense_19_loss: 0.0329 - dense_20_loss: 0.0906 - dense_21_loss: 0.0040 - dense_22_loss: 0.1074 - dense_23_loss: 0.0705 - dense_18_acc: 0.9500 - dense_19_acc: 0.9937 - dense_20_acc: 0.9750 - dense_21_acc: 1.0000 - dense_22_acc: 0.9750 - dense_23_acc: 0.98 - ETA: 14:18 - loss: 0.7004 - dense_18_loss: 0.2452 - dense_19_loss: 0.0526 - dense_20_loss: 0.1863 - dense_21_loss: 0.0043 - dense_22_loss: 0.1308 - dense_23_loss: 0.0813 - dense_18_acc: 0.9167 - dense_19_acc: 0.9896 - dense_20_acc: 0.9427 - dense_21_acc: 1.0000 - dense_22_acc: 0.9635 - dense_23_acc: 0.98 - ETA: 14:03 - loss: 0.7883 - dense_18_loss: 0.2623 - dense_19_loss: 0.0649 - dense_20_loss: 0.2010 - dense_21_loss: 0.0301 - dense_22_loss: 0.1586 - dense_23_loss: 0.0714 - dense_18_acc: 0.9062 - dense_19_acc: 0.9866 - dense_20_acc: 0.9375 - dense_21_acc: 0.9955 - dense_22_acc: 0.9509 - dense_23_acc: 0.98 - ETA: 13:51 - loss: 0.7174 - dense_18_loss: 0.2409 - dense_19_loss: 0.0576 - dense_20_loss: 0.1817 - dense_21_loss: 0.0270 - dense_22_loss: 0.1460 - dense_23_loss: 0.0641 - dense_18_acc: 0.9180 - dense_19_acc: 0.9883 - dense_20_acc: 0.9453 - dense_21_acc: 0.9961 - dense_22_acc: 0.9570 - dense_23_acc: 0.98 - ETA: 13:46 - loss: 0.6937 - dense_18_loss: 0.2292 - dense_19_loss: 0.0653 - dense_20_loss: 0.1736 - dense_21_loss: 0.0249 - dense_22_loss: 0.1420 - dense_23_loss: 0.0587 - dense_18_acc: 0.9236 - dense_19_acc: 0.9861 - dense_20_acc: 0.9479 - dense_21_acc: 0.9965 - dense_22_acc: 0.9583 - dense_23_acc: 0.98 - ETA: 13:45 - loss: 0.7392 - dense_18_loss: 0.2491 - dense_19_loss: 0.0714 - dense_20_loss: 0.1831 - dense_21_loss: 0.0232 - dense_22_loss: 0.1580 - dense_23_loss: 0.0544 - 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dense_23_loss: 0.0362 - dense_18_acc: 0.9302 - dense_19_acc: 0.9900 - dense_20_acc: 0.9614 - dense_21_acc: 0.9969 - dense_22_acc: 0.9598 - dense_23_acc: 0.991 - ETA: 1s - loss: 0.5162 - dense_18_loss: 0.1960 - dense_19_loss: 0.0343 - dense_20_loss: 0.1153 - dense_21_loss: 0.0170 - dense_22_loss: 0.1174 - dense_23_loss: 0.0362 - dense_18_acc: 0.9302 - dense_19_acc: 0.9900 - dense_20_acc: 0.9614 - dense_21_acc: 0.9969 - dense_22_acc: 0.9597 - dense_23_acc: 0.991 - ETA: 1s - loss: 0.5161 - dense_18_loss: 0.1960 - dense_19_loss: 0.0343 - dense_20_loss: 0.1153 - dense_21_loss: 0.0170 - dense_22_loss: 0.1174 - dense_23_loss: 0.0362 - dense_18_acc: 0.9302 - dense_19_acc: 0.9900 - dense_20_acc: 0.9614 - dense_21_acc: 0.9969 - dense_22_acc: 0.9598 - dense_23_acc: 0.9915102124/102124 [==============================] - ETA: 1s - loss: 0.5161 - dense_18_loss: 0.1960 - dense_19_loss: 0.0343 - dense_20_loss: 0.1153 - dense_21_loss: 0.0170 - dense_22_loss: 0.1174 - dense_23_loss: 0.0361 - dense_18_acc: 0.9301 - dense_19_acc: 0.9900 - dense_20_acc: 0.9614 - dense_21_acc: 0.9969 - dense_22_acc: 0.9597 - dense_23_acc: 0.991 - ETA: 0s - loss: 0.5161 - dense_18_loss: 0.1959 - dense_19_loss: 0.0343 - dense_20_loss: 0.1153 - dense_21_loss: 0.0170 - dense_22_loss: 0.1174 - dense_23_loss: 0.0361 - dense_18_acc: 0.9302 - dense_19_acc: 0.9901 - dense_20_acc: 0.9614 - dense_21_acc: 0.9969 - dense_22_acc: 0.9598 - dense_23_acc: 0.991 - ETA: 0s - loss: 0.5160 - dense_18_loss: 0.1959 - dense_19_loss: 0.0343 - dense_20_loss: 0.1153 - dense_21_loss: 0.0170 - dense_22_loss: 0.1174 - dense_23_loss: 0.0361 - dense_18_acc: 0.9302 - dense_19_acc: 0.9901 - dense_20_acc: 0.9614 - dense_21_acc: 0.9969 - dense_22_acc: 0.9598 - dense_23_acc: 0.991 - ETA: 0s - loss: 0.5159 - dense_18_loss: 0.1959 - dense_19_loss: 0.0343 - dense_20_loss: 0.1153 - dense_21_loss: 0.0170 - dense_22_loss: 0.1173 - dense_23_loss: 0.0361 - dense_18_acc: 0.9302 - dense_19_acc: 0.9901 - dense_20_acc: 0.9614 - dense_21_acc: 0.9969 - dense_22_acc: 0.9598 - dense_23_acc: 0.991 - ETA: 0s - loss: 0.5158 - dense_18_loss: 0.1959 - dense_19_loss: 0.0343 - dense_20_loss: 0.1152 - dense_21_loss: 0.0170 - dense_22_loss: 0.1173 - dense_23_loss: 0.0361 - dense_18_acc: 0.9302 - dense_19_acc: 0.9901 - dense_20_acc: 0.9614 - dense_21_acc: 0.9969 - dense_22_acc: 0.9598 - dense_23_acc: 0.991 - 832s 8ms/sample - loss: 0.5158 - dense_18_loss: 0.1959 - dense_19_loss: 0.0343 - dense_20_loss: 0.1152 - dense_21_loss: 0.0170 - dense_22_loss: 0.1173 - dense_23_loss: 0.0361 - dense_18_acc: 0.9302 - dense_19_acc: 0.9901 - dense_20_acc: 0.9614 - dense_21_acc: 0.9969 - dense_22_acc: 0.9598 - dense_23_acc: 0.9915 - val_loss: 0.4667 - val_dense_18_loss: 0.1752 - val_dense_19_loss: 0.0291 - val_dense_20_loss: 0.0967 - val_dense_21_loss: 0.0171 - val_dense_22_loss: 0.1072 - val_dense_23_loss: 0.0414 - val_dense_18_acc: 0.9388 - val_dense_19_acc: 0.9902 - val_dense_20_acc: 0.9681 - val_dense_21_acc: 0.9967 - val_dense_22_acc: 0.9636 - val_dense_23_acc: 0.9902 Epoch 2/5 864/102124 [..............................] - ETA: 11:55 - loss: 0.5345 - dense_18_loss: 0.1091 - dense_19_loss: 0.0056 - dense_20_loss: 0.2894 - dense_21_loss: 0.0026 - dense_22_loss: 0.1246 - dense_23_loss: 0.0032 - dense_18_acc: 0.9688 - dense_19_acc: 1.0000 - dense_20_acc: 0.9375 - dense_21_acc: 1.0000 - dense_22_acc: 0.9688 - dense_23_acc: 1.00 - ETA: 11:30 - loss: 0.2890 - dense_18_loss: 0.0661 - dense_19_loss: 0.0031 - dense_20_loss: 0.1500 - dense_21_loss: 0.0019 - dense_22_loss: 0.0659 - dense_23_loss: 0.0020 - dense_18_acc: 0.9844 - dense_19_acc: 1.0000 - dense_20_acc: 0.9688 - dense_21_acc: 1.0000 - dense_22_acc: 0.9844 - dense_23_acc: 1.00 - ETA: 11:35 - loss: 0.2530 - dense_18_loss: 0.0932 - dense_19_loss: 0.0024 - dense_20_loss: 0.1057 - dense_21_loss: 0.0017 - dense_22_loss: 0.0481 - dense_23_loss: 0.0018 - dense_18_acc: 0.9583 - dense_19_acc: 1.0000 - dense_20_acc: 0.9792 - dense_21_acc: 1.0000 - dense_22_acc: 0.9896 - dense_23_acc: 1.00 - ETA: 11:36 - loss: 0.3295 - dense_18_loss: 0.1594 - dense_19_loss: 0.0079 - dense_20_loss: 0.1113 - dense_21_loss: 0.0025 - dense_22_loss: 0.0454 - dense_23_loss: 0.0030 - dense_18_acc: 0.9375 - dense_19_acc: 1.0000 - dense_20_acc: 0.9766 - dense_21_acc: 1.0000 - dense_22_acc: 0.9844 - dense_23_acc: 1.00 - ETA: 11:37 - loss: 0.3836 - dense_18_loss: 0.1647 - dense_19_loss: 0.0446 - dense_20_loss: 0.1100 - dense_21_loss: 0.0022 - dense_22_loss: 0.0596 - dense_23_loss: 0.0025 - dense_18_acc: 0.9375 - dense_19_acc: 0.9937 - dense_20_acc: 0.9750 - dense_21_acc: 1.0000 - dense_22_acc: 0.9812 - dense_23_acc: 1.00 - ETA: 11:39 - loss: 0.3830 - dense_18_loss: 0.1566 - dense_19_loss: 0.0374 - dense_20_loss: 0.1017 - dense_21_loss: 0.0021 - dense_22_loss: 0.0610 - dense_23_loss: 0.0243 - dense_18_acc: 0.9375 - dense_19_acc: 0.9948 - dense_20_acc: 0.9740 - dense_21_acc: 1.0000 - dense_22_acc: 0.9792 - dense_23_acc: 0.99 - ETA: 11:37 - loss: 0.3577 - dense_18_loss: 0.1450 - dense_19_loss: 0.0361 - dense_20_loss: 0.0969 - dense_21_loss: 0.0023 - dense_22_loss: 0.0558 - dense_23_loss: 0.0216 - dense_18_acc: 0.9375 - dense_19_acc: 0.9911 - dense_20_acc: 0.9732 - dense_21_acc: 1.0000 - dense_22_acc: 0.9821 - dense_23_acc: 0.99 - ETA: 11:36 - loss: 0.3626 - dense_18_loss: 0.1500 - dense_19_loss: 0.0319 - dense_20_loss: 0.0888 - dense_21_loss: 0.0023 - dense_22_loss: 0.0705 - dense_23_loss: 0.0192 - dense_18_acc: 0.9375 - dense_19_acc: 0.9922 - dense_20_acc: 0.9766 - dense_21_acc: 1.0000 - dense_22_acc: 0.9766 - dense_23_acc: 0.99 - ETA: 11:34 - loss: 0.3457 - dense_18_loss: 0.1465 - dense_19_loss: 0.0292 - dense_20_loss: 0.0841 - dense_21_loss: 0.0024 - dense_22_loss: 0.0660 - dense_23_loss: 0.0174 - dense_18_acc: 0.9375 - dense_19_acc: 0.9931 - dense_20_acc: 0.9757 - dense_21_acc: 1.0000 - dense_22_acc: 0.9792 - dense_23_acc: 0.99 - ETA: 11:33 - loss: 0.3473 - dense_18_loss: 0.1457 - dense_19_loss: 0.0323 - dense_20_loss: 0.0783 - dense_21_loss: 0.0123 - dense_22_loss: 0.0626 - dense_23_loss: 0.0161 - dense_18_acc: 0.9406 - dense_19_acc: 0.9906 - dense_20_acc: 0.9781 - dense_21_acc: 0.9969 - dense_22_acc: 0.9812 - dense_23_acc: 0.99 - ETA: 11:34 - loss: 0.3356 - dense_18_loss: 0.1414 - dense_19_loss: 0.0307 - dense_20_loss: 0.0746 - dense_21_loss: 0.0115 - dense_22_loss: 0.0623 - dense_23_loss: 0.0151 - dense_18_acc: 0.9432 - dense_19_acc: 0.9915 - dense_20_acc: 0.9801 - dense_21_acc: 0.9972 - dense_22_acc: 0.9801 - dense_23_acc: 0.99 - ETA: 11:29 - loss: 0.3868 - dense_18_loss: 0.1516 - dense_19_loss: 0.0285 - dense_20_loss: 0.0955 - dense_21_loss: 0.0108 - dense_22_loss: 0.0863 - dense_23_loss: 0.0141 - dense_18_acc: 0.9375 - dense_19_acc: 0.9922 - dense_20_acc: 0.9714 - dense_21_acc: 0.9974 - dense_22_acc: 0.9714 - dense_23_acc: 0.99 - ETA: 11:27 - loss: 0.4295 - dense_18_loss: 0.1510 - dense_19_loss: 0.0290 - dense_20_loss: 0.1068 - dense_21_loss: 0.0105 - dense_22_loss: 0.0967 - dense_23_loss: 0.0356 - dense_18_acc: 0.9399 - dense_19_acc: 0.9904 - dense_20_acc: 0.9688 - dense_21_acc: 0.9976 - dense_22_acc: 0.9688 - dense_23_acc: 0.99 - ETA: 11:28 - loss: 0.4262 - dense_18_loss: 0.1522 - 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dense_22_acc: 0.9669 - dense_23_acc: 0.991 - ETA: 3s - loss: 0.4145 - dense_18_loss: 0.1569 - dense_19_loss: 0.0275 - dense_20_loss: 0.0866 - dense_21_loss: 0.0157 - dense_22_loss: 0.0954 - dense_23_loss: 0.0323 - dense_18_acc: 0.9448 - dense_19_acc: 0.9900 - dense_20_acc: 0.9720 - dense_21_acc: 0.9969 - dense_22_acc: 0.9669 - dense_23_acc: 0.991 - ETA: 3s - loss: 0.4144 - dense_18_loss: 0.1569 - dense_19_loss: 0.0275 - dense_20_loss: 0.0866 - dense_21_loss: 0.0157 - dense_22_loss: 0.0954 - dense_23_loss: 0.0323 - dense_18_acc: 0.9448 - dense_19_acc: 0.9900 - dense_20_acc: 0.9720 - dense_21_acc: 0.9969 - dense_22_acc: 0.9669 - dense_23_acc: 0.991 - ETA: 2s - loss: 0.4144 - dense_18_loss: 0.1569 - dense_19_loss: 0.0275 - dense_20_loss: 0.0866 - dense_21_loss: 0.0157 - dense_22_loss: 0.0954 - dense_23_loss: 0.0323 - dense_18_acc: 0.9448 - dense_19_acc: 0.9900 - dense_20_acc: 0.9720 - dense_21_acc: 0.9969 - dense_22_acc: 0.9669 - dense_23_acc: 0.991 - ETA: 2s - loss: 0.4143 - dense_18_loss: 0.1569 - dense_19_loss: 0.0275 - dense_20_loss: 0.0866 - dense_21_loss: 0.0157 - dense_22_loss: 0.0954 - dense_23_loss: 0.0323 - dense_18_acc: 0.9448 - dense_19_acc: 0.9900 - dense_20_acc: 0.9720 - dense_21_acc: 0.9969 - dense_22_acc: 0.9669 - dense_23_acc: 0.991 - ETA: 2s - loss: 0.4145 - dense_18_loss: 0.1569 - dense_19_loss: 0.0275 - dense_20_loss: 0.0866 - dense_21_loss: 0.0157 - dense_22_loss: 0.0954 - dense_23_loss: 0.0323 - dense_18_acc: 0.9448 - dense_19_acc: 0.9900 - dense_20_acc: 0.9720 - dense_21_acc: 0.9969 - dense_22_acc: 0.9669 - dense_23_acc: 0.991 - ETA: 2s - loss: 0.4144 - dense_18_loss: 0.1569 - dense_19_loss: 0.0275 - dense_20_loss: 0.0866 - dense_21_loss: 0.0157 - dense_22_loss: 0.0954 - dense_23_loss: 0.0323 - dense_18_acc: 0.9448 - dense_19_acc: 0.9900 - dense_20_acc: 0.9720 - dense_21_acc: 0.9969 - dense_22_acc: 0.9669 - dense_23_acc: 0.991 - ETA: 2s - loss: 0.4144 - dense_18_loss: 0.1569 - dense_19_loss: 0.0275 - dense_20_loss: 0.0866 - dense_21_loss: 0.0157 - dense_22_loss: 0.0954 - dense_23_loss: 0.0323 - dense_18_acc: 0.9448 - dense_19_acc: 0.9900 - dense_20_acc: 0.9720 - dense_21_acc: 0.9969 - dense_22_acc: 0.9669 - dense_23_acc: 0.991 - ETA: 1s - loss: 0.4143 - dense_18_loss: 0.1569 - dense_19_loss: 0.0275 - dense_20_loss: 0.0866 - dense_21_loss: 0.0157 - dense_22_loss: 0.0954 - dense_23_loss: 0.0323 - dense_18_acc: 0.9448 - dense_19_acc: 0.9900 - dense_20_acc: 0.9720 - dense_21_acc: 0.9969 - dense_22_acc: 0.9670 - dense_23_acc: 0.991 - ETA: 1s - loss: 0.4142 - dense_18_loss: 0.1568 - dense_19_loss: 0.0275 - dense_20_loss: 0.0866 - dense_21_loss: 0.0157 - dense_22_loss: 0.0954 - dense_23_loss: 0.0322 - dense_18_acc: 0.9448 - dense_19_acc: 0.9900 - dense_20_acc: 0.9720 - dense_21_acc: 0.9969 - dense_22_acc: 0.9670 - dense_23_acc: 0.991 - ETA: 1s - loss: 0.4142 - dense_18_loss: 0.1568 - dense_19_loss: 0.0275 - dense_20_loss: 0.0866 - dense_21_loss: 0.0157 - dense_22_loss: 0.0953 - dense_23_loss: 0.0322 - dense_18_acc: 0.9448 - dense_19_acc: 0.9901 - dense_20_acc: 0.9720 - dense_21_acc: 0.9969 - dense_22_acc: 0.9670 - dense_23_acc: 0.991 - ETA: 1s - loss: 0.4143 - dense_18_loss: 0.1568 - dense_19_loss: 0.0275 - dense_20_loss: 0.0866 - dense_21_loss: 0.0157 - dense_22_loss: 0.0954 - dense_23_loss: 0.0322 - dense_18_acc: 0.9448 - dense_19_acc: 0.9900 - dense_20_acc: 0.9720 - dense_21_acc: 0.9969 - dense_22_acc: 0.9669 - dense_23_acc: 0.9916102124/102124 [==============================] - ETA: 0s - loss: 0.4143 - dense_18_loss: 0.1568 - dense_19_loss: 0.0275 - dense_20_loss: 0.0866 - dense_21_loss: 0.0157 - dense_22_loss: 0.0954 - dense_23_loss: 0.0322 - dense_18_acc: 0.9448 - dense_19_acc: 0.9900 - dense_20_acc: 0.9720 - dense_21_acc: 0.9969 - dense_22_acc: 0.9670 - dense_23_acc: 0.991 - ETA: 0s - loss: 0.4142 - dense_18_loss: 0.1568 - dense_19_loss: 0.0275 - dense_20_loss: 0.0866 - dense_21_loss: 0.0157 - dense_22_loss: 0.0954 - dense_23_loss: 0.0322 - dense_18_acc: 0.9448 - dense_19_acc: 0.9900 - dense_20_acc: 0.9720 - dense_21_acc: 0.9969 - dense_22_acc: 0.9670 - dense_23_acc: 0.991 - ETA: 0s - loss: 0.4142 - dense_18_loss: 0.1568 - dense_19_loss: 0.0275 - dense_20_loss: 0.0866 - dense_21_loss: 0.0157 - dense_22_loss: 0.0954 - dense_23_loss: 0.0322 - dense_18_acc: 0.9448 - dense_19_acc: 0.9900 - dense_20_acc: 0.9720 - dense_21_acc: 0.9969 - dense_22_acc: 0.9670 - dense_23_acc: 0.991 - ETA: 0s - loss: 0.4141 - dense_18_loss: 0.1567 - dense_19_loss: 0.0275 - dense_20_loss: 0.0866 - dense_21_loss: 0.0157 - dense_22_loss: 0.0953 - dense_23_loss: 0.0322 - dense_18_acc: 0.9448 - dense_19_acc: 0.9900 - dense_20_acc: 0.9720 - dense_21_acc: 0.9969 - dense_22_acc: 0.9670 - dense_23_acc: 0.991 - ETA: 0s - loss: 0.4140 - dense_18_loss: 0.1567 - dense_19_loss: 0.0275 - dense_20_loss: 0.0865 - dense_21_loss: 0.0157 - dense_22_loss: 0.0954 - dense_23_loss: 0.0322 - dense_18_acc: 0.9448 - dense_19_acc: 0.9900 - dense_20_acc: 0.9721 - dense_21_acc: 0.9969 - dense_22_acc: 0.9670 - dense_23_acc: 0.991 - 783s 8ms/sample - loss: 0.4140 - dense_18_loss: 0.1567 - dense_19_loss: 0.0275 - dense_20_loss: 0.0865 - dense_21_loss: 0.0157 - dense_22_loss: 0.0953 - dense_23_loss: 0.0322 - dense_18_acc: 0.9448 - dense_19_acc: 0.9900 - dense_20_acc: 0.9721 - dense_21_acc: 0.9969 - dense_22_acc: 0.9670 - dense_23_acc: 0.9916 - val_loss: 0.4325 - val_dense_18_loss: 0.1632 - val_dense_19_loss: 0.0269 - val_dense_20_loss: 0.0913 - val_dense_21_loss: 0.0179 - val_dense_22_loss: 0.0973 - val_dense_23_loss: 0.0360 - val_dense_18_acc: 0.9440 - val_dense_19_acc: 0.9902 - val_dense_20_acc: 0.9710 - val_dense_21_acc: 0.9967 - val_dense_22_acc: 0.9654 - val_dense_23_acc: 0.9902 Epoch 3/5 864/102124 [..............................] - ETA: 11:40 - loss: 0.3794 - dense_18_loss: 0.1228 - dense_19_loss: 0.0300 - dense_20_loss: 0.1595 - dense_21_loss: 0.0025 - dense_22_loss: 0.0450 - dense_23_loss: 0.0197 - dense_18_acc: 0.9688 - dense_19_acc: 1.0000 - dense_20_acc: 0.9375 - dense_21_acc: 1.0000 - dense_22_acc: 1.0000 - dense_23_acc: 1.00 - ETA: 11:33 - loss: 0.3874 - dense_18_loss: 0.1130 - dense_19_loss: 0.0168 - dense_20_loss: 0.1435 - dense_21_loss: 0.0016 - dense_22_loss: 0.1002 - dense_23_loss: 0.0123 - dense_18_acc: 0.9688 - dense_19_acc: 1.0000 - dense_20_acc: 0.9531 - dense_21_acc: 1.0000 - dense_22_acc: 0.9688 - dense_23_acc: 1.00 - ETA: 11:31 - loss: 0.3207 - dense_18_loss: 0.1210 - dense_19_loss: 0.0125 - dense_20_loss: 0.1009 - dense_21_loss: 0.0014 - dense_22_loss: 0.0749 - dense_23_loss: 0.0100 - dense_18_acc: 0.9583 - dense_19_acc: 1.0000 - dense_20_acc: 0.9688 - dense_21_acc: 1.0000 - dense_22_acc: 0.9792 - dense_23_acc: 1.00 - ETA: 11:26 - loss: 0.3176 - dense_18_loss: 0.1119 - dense_19_loss: 0.0106 - dense_20_loss: 0.0941 - dense_21_loss: 0.0014 - dense_22_loss: 0.0758 - dense_23_loss: 0.0238 - dense_18_acc: 0.9531 - dense_19_acc: 1.0000 - dense_20_acc: 0.9688 - dense_21_acc: 1.0000 - dense_22_acc: 0.9766 - dense_23_acc: 0.99 - ETA: 11:21 - loss: 0.3969 - dense_18_loss: 0.1812 - dense_19_loss: 0.0091 - dense_20_loss: 0.0839 - dense_21_loss: 0.0014 - dense_22_loss: 0.1010 - dense_23_loss: 0.0203 - dense_18_acc: 0.9312 - dense_19_acc: 1.0000 - dense_20_acc: 0.9750 - dense_21_acc: 1.0000 - dense_22_acc: 0.9688 - dense_23_acc: 0.99 - ETA: 11:22 - loss: 0.3833 - dense_18_loss: 0.1818 - dense_19_loss: 0.0107 - dense_20_loss: 0.0769 - dense_21_loss: 0.0014 - dense_22_loss: 0.0878 - dense_23_loss: 0.0247 - dense_18_acc: 0.9323 - dense_19_acc: 1.0000 - dense_20_acc: 0.9740 - dense_21_acc: 1.0000 - dense_22_acc: 0.9740 - dense_23_acc: 0.98 - ETA: 11:25 - loss: 0.4088 - dense_18_loss: 0.1795 - dense_19_loss: 0.0097 - dense_20_loss: 0.0887 - dense_21_loss: 0.0013 - dense_22_loss: 0.0800 - dense_23_loss: 0.0497 - dense_18_acc: 0.9330 - dense_19_acc: 1.0000 - dense_20_acc: 0.9732 - dense_21_acc: 1.0000 - dense_22_acc: 0.9777 - dense_23_acc: 0.98 - ETA: 11:23 - loss: 0.4093 - dense_18_loss: 0.1782 - dense_19_loss: 0.0088 - dense_20_loss: 0.1020 - dense_21_loss: 0.0013 - dense_22_loss: 0.0752 - dense_23_loss: 0.0440 - dense_18_acc: 0.9336 - dense_19_acc: 1.0000 - dense_20_acc: 0.9727 - dense_21_acc: 1.0000 - dense_22_acc: 0.9766 - dense_23_acc: 0.98 - ETA: 11:19 - loss: 0.4182 - dense_18_loss: 0.1718 - dense_19_loss: 0.0083 - dense_20_loss: 0.0999 - dense_21_loss: 0.0013 - dense_22_loss: 0.0845 - dense_23_loss: 0.0525 - dense_18_acc: 0.9306 - dense_19_acc: 1.0000 - dense_20_acc: 0.9722 - dense_21_acc: 1.0000 - dense_22_acc: 0.9688 - dense_23_acc: 0.98 - ETA: 11:18 - loss: 0.3912 - dense_18_loss: 0.1601 - dense_19_loss: 0.0076 - dense_20_loss: 0.0940 - dense_21_loss: 0.0012 - dense_22_loss: 0.0806 - dense_23_loss: 0.0475 - dense_18_acc: 0.9375 - dense_19_acc: 1.0000 - dense_20_acc: 0.9719 - dense_21_acc: 1.0000 - dense_22_acc: 0.9688 - dense_23_acc: 0.98 - ETA: 11:18 - loss: 0.3718 - dense_18_loss: 0.1498 - dense_19_loss: 0.0074 - dense_20_loss: 0.0908 - dense_21_loss: 0.0012 - dense_22_loss: 0.0787 - dense_23_loss: 0.0439 - dense_18_acc: 0.9432 - dense_19_acc: 1.0000 - dense_20_acc: 0.9716 - dense_21_acc: 1.0000 - dense_22_acc: 0.9688 - dense_23_acc: 0.98 - ETA: 11:19 - loss: 0.3718 - dense_18_loss: 0.1502 - dense_19_loss: 0.0076 - dense_20_loss: 0.0889 - dense_21_loss: 0.0013 - dense_22_loss: 0.0826 - dense_23_loss: 0.0411 - dense_18_acc: 0.9427 - dense_19_acc: 1.0000 - dense_20_acc: 0.9740 - dense_21_acc: 1.0000 - dense_22_acc: 0.9635 - dense_23_acc: 0.98 - ETA: 11:20 - loss: 0.3917 - dense_18_loss: 0.1501 - dense_19_loss: 0.0097 - dense_20_loss: 0.0979 - dense_21_loss: 0.0014 - dense_22_loss: 0.0931 - dense_23_loss: 0.0396 - dense_18_acc: 0.9447 - dense_19_acc: 0.9976 - dense_20_acc: 0.9712 - dense_21_acc: 1.0000 - dense_22_acc: 0.9591 - dense_23_acc: 0.99 - ETA: 11:21 - loss: 0.4056 - dense_18_loss: 0.1585 - dense_19_loss: 0.0092 - dense_20_loss: 0.1085 - dense_21_loss: 0.0014 - dense_22_loss: 0.0910 - dense_23_loss: 0.0371 - dense_18_acc: 0.9397 - dense_19_acc: 0.9978 - dense_20_acc: 0.9665 - dense_21_acc: 1.0000 - dense_22_acc: 0.9598 - dense_23_acc: 0.99 - ETA: 11:20 - loss: 0.4094 - dense_18_loss: 0.1541 - dense_19_loss: 0.0094 - dense_20_loss: 0.1151 - dense_21_loss: 0.0015 - dense_22_loss: 0.0937 - dense_23_loss: 0.0357 - dense_18_acc: 0.9417 - dense_19_acc: 0.9979 - dense_20_acc: 0.9625 - dense_21_acc: 1.0000 - dense_22_acc: 0.9583 - dense_23_acc: 0.99 - ETA: 11:21 - loss: 0.4392 - dense_18_loss: 0.1654 - dense_19_loss: 0.0104 - dense_20_loss: 0.1173 - dense_21_loss: 0.0016 - dense_22_loss: 0.1021 - dense_23_loss: 0.0424 - dense_18_acc: 0.9375 - dense_19_acc: 0.9980 - dense_20_acc: 0.9629 - dense_21_acc: 1.0000 - dense_22_acc: 0.9570 - dense_23_acc: 0.99 - ETA: 11:23 - loss: 0.4437 - dense_18_loss: 0.1665 - dense_19_loss: 0.0107 - dense_20_loss: 0.1133 - dense_21_loss: 0.0016 - dense_22_loss: 0.1059 - dense_23_loss: 0.0458 - dense_18_acc: 0.9393 - dense_19_acc: 0.9982 - dense_20_acc: 0.9632 - dense_21_acc: 1.0000 - dense_22_acc: 0.9540 - dense_23_acc: 0.98 - ETA: 11:22 - loss: 0.4465 - dense_18_loss: 0.1654 - dense_19_loss: 0.0104 - dense_20_loss: 0.1143 - dense_21_loss: 0.0016 - dense_22_loss: 0.1110 - dense_23_loss: 0.0438 - dense_18_acc: 0.9392 - dense_19_acc: 0.9983 - dense_20_acc: 0.9618 - dense_21_acc: 1.0000 - dense_22_acc: 0.9514 - dense_23_acc: 0.98 - ETA: 11:20 - loss: 0.4317 - dense_18_loss: 0.1617 - dense_19_loss: 0.0104 - dense_20_loss: 0.1092 - dense_21_loss: 0.0016 - dense_22_loss: 0.1067 - dense_23_loss: 0.0422 - dense_18_acc: 0.9408 - dense_19_acc: 0.9984 - dense_20_acc: 0.9638 - dense_21_acc: 1.0000 - dense_22_acc: 0.9539 - dense_23_acc: 0.99 - ETA: 11:19 - loss: 0.4368 - dense_18_loss: 0.1613 - dense_19_loss: 0.0132 - dense_20_loss: 0.1054 - dense_21_loss: 0.0017 - dense_22_loss: 0.1055 - dense_23_loss: 0.0497 - dense_18_acc: 0.9422 - dense_19_acc: 0.9969 - dense_20_acc: 0.9656 - dense_21_acc: 1.0000 - dense_22_acc: 0.9547 - dense_23_acc: 0.98 - ETA: 11:19 - loss: 0.4179 - dense_18_loss: 0.1548 - dense_19_loss: 0.0126 - dense_20_loss: 0.1007 - dense_21_loss: 0.0016 - dense_22_loss: 0.1009 - dense_23_loss: 0.0474 - dense_18_acc: 0.9449 - dense_19_acc: 0.9970 - dense_20_acc: 0.9673 - dense_21_acc: 1.0000 - dense_22_acc: 0.9568 - dense_23_acc: 0.98 - ETA: 11:20 - loss: 0.4072 - dense_18_loss: 0.1518 - dense_19_loss: 0.0121 - dense_20_loss: 0.0979 - dense_21_loss: 0.0015 - dense_22_loss: 0.0985 - dense_23_loss: 0.0454 - dense_18_acc: 0.9460 - dense_19_acc: 0.9972 - dense_20_acc: 0.9673 - dense_21_acc: 1.0000 - dense_22_acc: 0.9574 - dense_23_acc: 0.99 - ETA: 11:20 - loss: 0.4091 - dense_18_loss: 0.1579 - dense_19_loss: 0.0120 - dense_20_loss: 0.0966 - dense_21_loss: 0.0016 - dense_22_loss: 0.0971 - dense_23_loss: 0.0439 - dense_18_acc: 0.9443 - dense_19_acc: 0.9973 - dense_20_acc: 0.9674 - dense_21_acc: 1.0000 - dense_22_acc: 0.9579 - dense_23_acc: 0.99 - ETA: 11:21 - loss: 0.4000 - dense_18_loss: 0.1550 - dense_19_loss: 0.0118 - dense_20_loss: 0.0943 - dense_21_loss: 0.0015 - dense_22_loss: 0.0949 - dense_23_loss: 0.0424 - dense_18_acc: 0.9453 - dense_19_acc: 0.9974 - dense_20_acc: 0.9674 - dense_21_acc: 1.0000 - dense_22_acc: 0.9596 - dense_23_acc: 0.99 - ETA: 11:20 - loss: 0.4080 - dense_18_loss: 0.1580 - dense_19_loss: 0.0116 - dense_20_loss: 0.0997 - 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dense_18_acc: 0.9483 - dense_19_acc: 0.9905 - dense_20_acc: 0.9742 - dense_21_acc: 0.9969 - dense_22_acc: 0.9682 - dense_23_acc: 0.9916102124/102124 [==============================] - ETA: 0s - loss: 0.3837 - dense_18_loss: 0.1449 - dense_19_loss: 0.0256 - dense_20_loss: 0.0783 - dense_21_loss: 0.0153 - dense_22_loss: 0.0886 - dense_23_loss: 0.0309 - dense_18_acc: 0.9483 - dense_19_acc: 0.9905 - dense_20_acc: 0.9742 - dense_21_acc: 0.9969 - dense_22_acc: 0.9682 - dense_23_acc: 0.991 - ETA: 0s - loss: 0.3836 - dense_18_loss: 0.1449 - dense_19_loss: 0.0256 - dense_20_loss: 0.0784 - dense_21_loss: 0.0153 - dense_22_loss: 0.0886 - dense_23_loss: 0.0309 - dense_18_acc: 0.9483 - dense_19_acc: 0.9905 - dense_20_acc: 0.9742 - dense_21_acc: 0.9969 - dense_22_acc: 0.9682 - dense_23_acc: 0.991 - ETA: 0s - loss: 0.3836 - dense_18_loss: 0.1449 - dense_19_loss: 0.0256 - dense_20_loss: 0.0783 - dense_21_loss: 0.0153 - dense_22_loss: 0.0886 - dense_23_loss: 0.0309 - dense_18_acc: 0.9483 - dense_19_acc: 0.9905 - dense_20_acc: 0.9742 - dense_21_acc: 0.9969 - dense_22_acc: 0.9682 - dense_23_acc: 0.991 - ETA: 0s - loss: 0.3836 - dense_18_loss: 0.1449 - dense_19_loss: 0.0256 - dense_20_loss: 0.0783 - dense_21_loss: 0.0153 - dense_22_loss: 0.0886 - dense_23_loss: 0.0308 - dense_18_acc: 0.9483 - dense_19_acc: 0.9905 - dense_20_acc: 0.9742 - dense_21_acc: 0.9969 - dense_22_acc: 0.9682 - dense_23_acc: 0.991 - ETA: 0s - loss: 0.3836 - dense_18_loss: 0.1449 - dense_19_loss: 0.0256 - dense_20_loss: 0.0783 - dense_21_loss: 0.0153 - dense_22_loss: 0.0886 - dense_23_loss: 0.0308 - dense_18_acc: 0.9483 - dense_19_acc: 0.9905 - dense_20_acc: 0.9742 - dense_21_acc: 0.9969 - dense_22_acc: 0.9682 - dense_23_acc: 0.991 - 782s 8ms/sample - loss: 0.3836 - dense_18_loss: 0.1449 - dense_19_loss: 0.0256 - dense_20_loss: 0.0783 - dense_21_loss: 0.0153 - dense_22_loss: 0.0887 - dense_23_loss: 0.0309 - dense_18_acc: 0.9483 - dense_19_acc: 0.9905 - dense_20_acc: 0.9742 - dense_21_acc: 0.9969 - dense_22_acc: 0.9682 - dense_23_acc: 0.9916 - val_loss: 0.4019 - val_dense_18_loss: 0.1479 - val_dense_19_loss: 0.0257 - val_dense_20_loss: 0.0837 - val_dense_21_loss: 0.0165 - val_dense_22_loss: 0.0933 - val_dense_23_loss: 0.0349 - val_dense_18_acc: 0.9476 - val_dense_19_acc: 0.9904 - val_dense_20_acc: 0.9718 - val_dense_21_acc: 0.9967 - val_dense_22_acc: 0.9660 - val_dense_23_acc: 0.9901 Epoch 4/5 864/102124 [..............................] - ETA: 11:30 - loss: 0.1675 - dense_18_loss: 0.1223 - dense_19_loss: 0.0014 - dense_20_loss: 0.0187 - dense_21_loss: 0.0013 - dense_22_loss: 0.0210 - dense_23_loss: 0.0026 - dense_18_acc: 0.9375 - dense_19_acc: 1.0000 - dense_20_acc: 1.0000 - dense_21_acc: 1.0000 - dense_22_acc: 1.0000 - dense_23_acc: 1.00 - ETA: 11:39 - loss: 0.2138 - dense_18_loss: 0.0944 - dense_19_loss: 0.0164 - dense_20_loss: 0.0324 - dense_21_loss: 0.0039 - dense_22_loss: 0.0352 - dense_23_loss: 0.0315 - dense_18_acc: 0.9688 - dense_19_acc: 1.0000 - dense_20_acc: 0.9844 - dense_21_acc: 1.0000 - dense_22_acc: 1.0000 - dense_23_acc: 0.98 - ETA: 11:36 - loss: 0.1602 - dense_18_loss: 0.0731 - dense_19_loss: 0.0111 - dense_20_loss: 0.0248 - dense_21_loss: 0.0029 - dense_22_loss: 0.0269 - dense_23_loss: 0.0214 - dense_18_acc: 0.9792 - dense_19_acc: 1.0000 - dense_20_acc: 0.9896 - dense_21_acc: 1.0000 - dense_22_acc: 1.0000 - dense_23_acc: 0.98 - ETA: 11:33 - loss: 0.2027 - dense_18_loss: 0.0777 - dense_19_loss: 0.0089 - dense_20_loss: 0.0372 - dense_21_loss: 0.0026 - dense_22_loss: 0.0593 - dense_23_loss: 0.0170 - dense_18_acc: 0.9766 - dense_19_acc: 1.0000 - dense_20_acc: 0.9844 - dense_21_acc: 1.0000 - dense_22_acc: 0.9922 - dense_23_acc: 0.99 - ETA: 11:25 - loss: 0.1972 - dense_18_loss: 0.0687 - dense_19_loss: 0.0090 - dense_20_loss: 0.0364 - dense_21_loss: 0.0029 - dense_22_loss: 0.0548 - dense_23_loss: 0.0253 - dense_18_acc: 0.9812 - dense_19_acc: 1.0000 - dense_20_acc: 0.9875 - dense_21_acc: 1.0000 - dense_22_acc: 0.9937 - dense_23_acc: 0.98 - ETA: 11:25 - loss: 0.2192 - dense_18_loss: 0.0639 - dense_19_loss: 0.0088 - dense_20_loss: 0.0448 - dense_21_loss: 0.0179 - dense_22_loss: 0.0614 - dense_23_loss: 0.0225 - dense_18_acc: 0.9844 - dense_19_acc: 1.0000 - dense_20_acc: 0.9844 - dense_21_acc: 0.9948 - dense_22_acc: 0.9896 - dense_23_acc: 0.98 - ETA: 11:28 - loss: 0.2412 - dense_18_loss: 0.0743 - dense_19_loss: 0.0188 - dense_20_loss: 0.0450 - dense_21_loss: 0.0163 - dense_22_loss: 0.0606 - dense_23_loss: 0.0262 - dense_18_acc: 0.9777 - dense_19_acc: 0.9955 - dense_20_acc: 0.9866 - dense_21_acc: 0.9955 - dense_22_acc: 0.9911 - dense_23_acc: 0.98 - ETA: 11:29 - loss: 0.2285 - dense_18_loss: 0.0777 - dense_19_loss: 0.0166 - dense_20_loss: 0.0413 - dense_21_loss: 0.0144 - dense_22_loss: 0.0553 - dense_23_loss: 0.0232 - dense_18_acc: 0.9766 - dense_19_acc: 0.9961 - dense_20_acc: 0.9883 - dense_21_acc: 0.9961 - dense_22_acc: 0.9922 - dense_23_acc: 0.98 - ETA: 11:30 - loss: 0.2939 - dense_18_loss: 0.1063 - dense_19_loss: 0.0211 - dense_20_loss: 0.0564 - dense_21_loss: 0.0132 - dense_22_loss: 0.0748 - dense_23_loss: 0.0220 - dense_18_acc: 0.9722 - dense_19_acc: 0.9931 - dense_20_acc: 0.9826 - dense_21_acc: 0.9965 - dense_22_acc: 0.9826 - dense_23_acc: 0.98 - ETA: 11:30 - loss: 0.3139 - dense_18_loss: 0.1163 - dense_19_loss: 0.0193 - dense_20_loss: 0.0650 - dense_21_loss: 0.0121 - dense_22_loss: 0.0810 - dense_23_loss: 0.0203 - dense_18_acc: 0.9625 - dense_19_acc: 0.9937 - dense_20_acc: 0.9750 - dense_21_acc: 0.9969 - dense_22_acc: 0.9750 - dense_23_acc: 0.99 - ETA: 11:28 - loss: 0.3157 - dense_18_loss: 0.1189 - dense_19_loss: 0.0179 - dense_20_loss: 0.0619 - dense_21_loss: 0.0112 - dense_22_loss: 0.0807 - dense_23_loss: 0.0251 - dense_18_acc: 0.9631 - dense_19_acc: 0.9943 - dense_20_acc: 0.9773 - dense_21_acc: 0.9972 - dense_22_acc: 0.9744 - dense_23_acc: 0.98 - ETA: 11:28 - loss: 0.3140 - dense_18_loss: 0.1217 - dense_19_loss: 0.0165 - dense_20_loss: 0.0669 - dense_21_loss: 0.0104 - dense_22_loss: 0.0753 - dense_23_loss: 0.0232 - dense_18_acc: 0.9609 - dense_19_acc: 0.9948 - dense_20_acc: 0.9766 - dense_21_acc: 0.9974 - dense_22_acc: 0.9766 - dense_23_acc: 0.98 - ETA: 11:28 - loss: 0.3033 - dense_18_loss: 0.1153 - dense_19_loss: 0.0159 - dense_20_loss: 0.0643 - dense_21_loss: 0.0099 - dense_22_loss: 0.0724 - dense_23_loss: 0.0256 - dense_18_acc: 0.9639 - dense_19_acc: 0.9952 - dense_20_acc: 0.9784 - dense_21_acc: 0.9976 - dense_22_acc: 0.9784 - dense_23_acc: 0.98 - ETA: 11:27 - loss: 0.2975 - dense_18_loss: 0.1123 - dense_19_loss: 0.0156 - dense_20_loss: 0.0629 - dense_21_loss: 0.0095 - dense_22_loss: 0.0727 - dense_23_loss: 0.0245 - dense_18_acc: 0.9643 - dense_19_acc: 0.9955 - dense_20_acc: 0.9777 - dense_21_acc: 0.9978 - dense_22_acc: 0.9777 - dense_23_acc: 0.98 - ETA: 11:26 - loss: 0.3062 - dense_18_loss: 0.1123 - dense_19_loss: 0.0148 - dense_20_loss: 0.0767 - dense_21_loss: 0.0090 - dense_22_loss: 0.0701 - dense_23_loss: 0.0232 - dense_18_acc: 0.9625 - dense_19_acc: 0.9958 - dense_20_acc: 0.9729 - dense_21_acc: 0.9979 - dense_22_acc: 0.9792 - dense_23_acc: 0.98 - ETA: 11:24 - loss: 0.3117 - dense_18_loss: 0.1210 - dense_19_loss: 0.0141 - dense_20_loss: 0.0759 - dense_21_loss: 0.0087 - dense_22_loss: 0.0697 - dense_23_loss: 0.0223 - dense_18_acc: 0.9590 - dense_19_acc: 0.9961 - dense_20_acc: 0.9707 - dense_21_acc: 0.9980 - dense_22_acc: 0.9785 - dense_23_acc: 0.99 - ETA: 11:23 - loss: 0.3187 - dense_18_loss: 0.1318 - dense_19_loss: 0.0136 - dense_20_loss: 0.0730 - dense_21_loss: 0.0083 - dense_22_loss: 0.0705 - dense_23_loss: 0.0215 - dense_18_acc: 0.9577 - dense_19_acc: 0.9963 - dense_20_acc: 0.9724 - dense_21_acc: 0.9982 - dense_22_acc: 0.9779 - dense_23_acc: 0.99 - ETA: 11:23 - loss: 0.3157 - dense_18_loss: 0.1290 - dense_19_loss: 0.0157 - dense_20_loss: 0.0703 - dense_21_loss: 0.0081 - dense_22_loss: 0.0711 - dense_23_loss: 0.0214 - dense_18_acc: 0.9583 - dense_19_acc: 0.9948 - dense_20_acc: 0.9740 - dense_21_acc: 0.9983 - dense_22_acc: 0.9774 - dense_23_acc: 0.99 - ETA: 11:23 - loss: 0.3177 - dense_18_loss: 0.1329 - dense_19_loss: 0.0150 - dense_20_loss: 0.0711 - dense_21_loss: 0.0078 - dense_22_loss: 0.0702 - dense_23_loss: 0.0206 - dense_18_acc: 0.9539 - dense_19_acc: 0.9951 - dense_20_acc: 0.9737 - dense_21_acc: 0.9984 - dense_22_acc: 0.9786 - dense_23_acc: 0.99 - ETA: 11:23 - loss: 0.3178 - dense_18_loss: 0.1346 - dense_19_loss: 0.0145 - dense_20_loss: 0.0712 - dense_21_loss: 0.0076 - dense_22_loss: 0.0698 - dense_23_loss: 0.0201 - dense_18_acc: 0.9531 - dense_19_acc: 0.9953 - dense_20_acc: 0.9734 - dense_21_acc: 0.9984 - dense_22_acc: 0.9781 - dense_23_acc: 0.99 - ETA: 11:22 - loss: 0.3248 - dense_18_loss: 0.1328 - dense_19_loss: 0.0148 - dense_20_loss: 0.0751 - dense_21_loss: 0.0075 - dense_22_loss: 0.0726 - dense_23_loss: 0.0219 - dense_18_acc: 0.9539 - dense_19_acc: 0.9955 - dense_20_acc: 0.9717 - dense_21_acc: 0.9985 - dense_22_acc: 0.9762 - dense_23_acc: 0.99 - ETA: 11:21 - loss: 0.3175 - dense_18_loss: 0.1313 - dense_19_loss: 0.0142 - dense_20_loss: 0.0730 - dense_21_loss: 0.0073 - dense_22_loss: 0.0706 - dense_23_loss: 0.0211 - dense_18_acc: 0.9545 - dense_19_acc: 0.9957 - dense_20_acc: 0.9730 - dense_21_acc: 0.9986 - dense_22_acc: 0.9773 - dense_23_acc: 0.99 - ETA: 11:22 - loss: 0.3353 - dense_18_loss: 0.1373 - dense_19_loss: 0.0140 - dense_20_loss: 0.0764 - dense_21_loss: 0.0071 - dense_22_loss: 0.0798 - dense_23_loss: 0.0208 - dense_18_acc: 0.9524 - dense_19_acc: 0.9959 - dense_20_acc: 0.9728 - dense_21_acc: 0.9986 - dense_22_acc: 0.9728 - dense_23_acc: 0.99 - ETA: 11:22 - loss: 0.3432 - dense_18_loss: 0.1392 - dense_19_loss: 0.0156 - dense_20_loss: 0.0783 - dense_21_loss: 0.0072 - dense_22_loss: 0.0820 - dense_23_loss: 0.0210 - dense_18_acc: 0.9518 - dense_19_acc: 0.9948 - dense_20_acc: 0.9727 - dense_21_acc: 0.9987 - dense_22_acc: 0.9714 - dense_23_acc: 0.99 - ETA: 11:22 - loss: 0.3320 - dense_18_loss: 0.1350 - dense_19_loss: 0.0150 - dense_20_loss: 0.0756 - dense_21_loss: 0.0069 - dense_22_loss: 0.0792 - dense_23_loss: 0.0202 - dense_18_acc: 0.9538 - dense_19_acc: 0.9950 - dense_20_acc: 0.9737 - dense_21_acc: 0.9987 - dense_22_acc: 0.9725 - dense_23_acc: 0.99 - ETA: 11:21 - loss: 0.3418 - dense_18_loss: 0.1363 - dense_19_loss: 0.0148 - dense_20_loss: 0.0793 - dense_21_loss: 0.0068 - dense_22_loss: 0.0845 - dense_23_loss: 0.0200 - dense_18_acc: 0.9531 - dense_19_acc: 0.9952 - dense_20_acc: 0.9724 - dense_21_acc: 0.9988 - dense_22_acc: 0.9688 - dense_23_acc: 0.99 - ETA: 11:20 - loss: 0.3375 - dense_18_loss: 0.1361 - dense_19_loss: 0.0143 - dense_20_loss: 0.0768 - dense_21_loss: 0.0066 - dense_22_loss: 0.0842 - dense_23_loss: 0.0194 - dense_18_acc: 0.9525 - dense_19_acc: 0.9954 - dense_20_acc: 0.9734 - dense_21_acc: 0.9988 - dense_22_acc: 0.9688 - dense_23_acc: 0.9931 1728/102124 [..............................] - ETA: 11:21 - loss: 0.3491 - dense_18_loss: 0.1441 - dense_19_loss: 0.0139 - dense_20_loss: 0.0774 - dense_21_loss: 0.0102 - dense_22_loss: 0.0847 - dense_23_loss: 0.0188 - dense_18_acc: 0.9498 - dense_19_acc: 0.9955 - dense_20_acc: 0.9732 - dense_21_acc: 0.9978 - dense_22_acc: 0.9688 - dense_23_acc: 0.99 - ETA: 11:21 - loss: 0.3635 - dense_18_loss: 0.1486 - dense_19_loss: 0.0144 - dense_20_loss: 0.0778 - dense_21_loss: 0.0139 - dense_22_loss: 0.0897 - dense_23_loss: 0.0190 - dense_18_acc: 0.9483 - dense_19_acc: 0.9957 - dense_20_acc: 0.9731 - dense_21_acc: 0.9968 - dense_22_acc: 0.9677 - dense_23_acc: 0.99 - ETA: 11:21 - loss: 0.3556 - dense_18_loss: 0.1472 - dense_19_loss: 0.0140 - dense_20_loss: 0.0755 - dense_21_loss: 0.0135 - dense_22_loss: 0.0870 - dense_23_loss: 0.0184 - dense_18_acc: 0.9490 - dense_19_acc: 0.9958 - dense_20_acc: 0.9740 - dense_21_acc: 0.9969 - dense_22_acc: 0.9688 - dense_23_acc: 0.99 - ETA: 11:21 - loss: 0.3540 - dense_18_loss: 0.1494 - dense_19_loss: 0.0135 - dense_20_loss: 0.0757 - dense_21_loss: 0.0131 - dense_22_loss: 0.0845 - dense_23_loss: 0.0179 - dense_18_acc: 0.9486 - dense_19_acc: 0.9960 - dense_20_acc: 0.9738 - dense_21_acc: 0.9970 - dense_22_acc: 0.9698 - dense_23_acc: 0.99 - ETA: 11:20 - loss: 0.3497 - dense_18_loss: 0.1486 - dense_19_loss: 0.0132 - dense_20_loss: 0.0744 - dense_21_loss: 0.0127 - dense_22_loss: 0.0832 - dense_23_loss: 0.0175 - dense_18_acc: 0.9492 - dense_19_acc: 0.9961 - dense_20_acc: 0.9746 - dense_21_acc: 0.9971 - dense_22_acc: 0.9707 - dense_23_acc: 0.99 - ETA: 11:18 - loss: 0.3487 - dense_18_loss: 0.1473 - dense_19_loss: 0.0144 - dense_20_loss: 0.0751 - dense_21_loss: 0.0125 - dense_22_loss: 0.0820 - dense_23_loss: 0.0174 - dense_18_acc: 0.9498 - dense_19_acc: 0.9953 - dense_20_acc: 0.9744 - dense_21_acc: 0.9972 - dense_22_acc: 0.9706 - dense_23_acc: 0.99 - ETA: 11:19 - loss: 0.3539 - dense_18_loss: 0.1458 - dense_19_loss: 0.0153 - dense_20_loss: 0.0758 - dense_21_loss: 0.0174 - dense_22_loss: 0.0826 - dense_23_loss: 0.0171 - dense_18_acc: 0.9504 - dense_19_acc: 0.9945 - dense_20_acc: 0.9743 - dense_21_acc: 0.9963 - dense_22_acc: 0.9706 - dense_23_acc: 0.99 - ETA: 11:20 - loss: 0.3528 - dense_18_loss: 0.1435 - dense_19_loss: 0.0149 - dense_20_loss: 0.0774 - dense_21_loss: 0.0169 - dense_22_loss: 0.0833 - dense_23_loss: 0.0168 - dense_18_acc: 0.9509 - dense_19_acc: 0.9946 - dense_20_acc: 0.9732 - 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dense_21_acc: 0.9969 - dense_22_acc: 0.9695 - dense_23_acc: 0.991 - ETA: 2s - loss: 0.3575 - dense_18_loss: 0.1353 - dense_19_loss: 0.0241 - dense_20_loss: 0.0714 - dense_21_loss: 0.0147 - dense_22_loss: 0.0830 - dense_23_loss: 0.0289 - dense_18_acc: 0.9515 - dense_19_acc: 0.9906 - dense_20_acc: 0.9758 - dense_21_acc: 0.9969 - dense_22_acc: 0.9695 - dense_23_acc: 0.991 - ETA: 2s - loss: 0.3576 - dense_18_loss: 0.1353 - dense_19_loss: 0.0241 - dense_20_loss: 0.0715 - dense_21_loss: 0.0147 - dense_22_loss: 0.0830 - dense_23_loss: 0.0289 - dense_18_acc: 0.9515 - dense_19_acc: 0.9906 - dense_20_acc: 0.9758 - dense_21_acc: 0.9969 - dense_22_acc: 0.9695 - dense_23_acc: 0.991 - ETA: 2s - loss: 0.3575 - dense_18_loss: 0.1353 - dense_19_loss: 0.0241 - dense_20_loss: 0.0715 - dense_21_loss: 0.0147 - dense_22_loss: 0.0830 - dense_23_loss: 0.0289 - dense_18_acc: 0.9515 - dense_19_acc: 0.9906 - dense_20_acc: 0.9758 - dense_21_acc: 0.9969 - dense_22_acc: 0.9695 - dense_23_acc: 0.991 - ETA: 2s - loss: 0.3574 - dense_18_loss: 0.1353 - dense_19_loss: 0.0241 - dense_20_loss: 0.0715 - dense_21_loss: 0.0147 - dense_22_loss: 0.0830 - dense_23_loss: 0.0289 - dense_18_acc: 0.9515 - dense_19_acc: 0.9906 - dense_20_acc: 0.9758 - dense_21_acc: 0.9969 - dense_22_acc: 0.9695 - dense_23_acc: 0.991 - ETA: 1s - loss: 0.3573 - dense_18_loss: 0.1353 - dense_19_loss: 0.0241 - dense_20_loss: 0.0714 - dense_21_loss: 0.0147 - dense_22_loss: 0.0830 - dense_23_loss: 0.0289 - dense_18_acc: 0.9515 - dense_19_acc: 0.9906 - dense_20_acc: 0.9758 - dense_21_acc: 0.9969 - dense_22_acc: 0.9695 - dense_23_acc: 0.991 - ETA: 1s - loss: 0.3573 - dense_18_loss: 0.1352 - dense_19_loss: 0.0241 - dense_20_loss: 0.0714 - dense_21_loss: 0.0147 - dense_22_loss: 0.0830 - dense_23_loss: 0.0288 - dense_18_acc: 0.9515 - dense_19_acc: 0.9906 - dense_20_acc: 0.9758 - dense_21_acc: 0.9969 - dense_22_acc: 0.9695 - dense_23_acc: 0.991 - ETA: 1s - loss: 0.3573 - dense_18_loss: 0.1353 - dense_19_loss: 0.0241 - dense_20_loss: 0.0714 - dense_21_loss: 0.0147 - dense_22_loss: 0.0830 - dense_23_loss: 0.0288 - dense_18_acc: 0.9515 - dense_19_acc: 0.9906 - dense_20_acc: 0.9758 - dense_21_acc: 0.9969 - dense_22_acc: 0.9695 - dense_23_acc: 0.991 - ETA: 1s - loss: 0.3572 - dense_18_loss: 0.1352 - dense_19_loss: 0.0241 - dense_20_loss: 0.0714 - dense_21_loss: 0.0147 - dense_22_loss: 0.0830 - dense_23_loss: 0.0288 - dense_18_acc: 0.9515 - dense_19_acc: 0.9906 - dense_20_acc: 0.9758 - dense_21_acc: 0.9969 - dense_22_acc: 0.9695 - dense_23_acc: 0.991 - ETA: 1s - loss: 0.3573 - dense_18_loss: 0.1352 - dense_19_loss: 0.0240 - dense_20_loss: 0.0715 - dense_21_loss: 0.0147 - dense_22_loss: 0.0830 - dense_23_loss: 0.0289 - dense_18_acc: 0.9515 - dense_19_acc: 0.9906 - dense_20_acc: 0.9758 - dense_21_acc: 0.9969 - dense_22_acc: 0.9695 - dense_23_acc: 0.9916102124/102124 [==============================] - ETA: 0s - loss: 0.3572 - dense_18_loss: 0.1352 - dense_19_loss: 0.0240 - dense_20_loss: 0.0714 - dense_21_loss: 0.0147 - dense_22_loss: 0.0830 - dense_23_loss: 0.0289 - dense_18_acc: 0.9515 - dense_19_acc: 0.9906 - dense_20_acc: 0.9758 - dense_21_acc: 0.9969 - dense_22_acc: 0.9695 - dense_23_acc: 0.991 - ETA: 0s - loss: 0.3572 - dense_18_loss: 0.1352 - dense_19_loss: 0.0240 - dense_20_loss: 0.0714 - dense_21_loss: 0.0147 - dense_22_loss: 0.0830 - dense_23_loss: 0.0289 - dense_18_acc: 0.9515 - dense_19_acc: 0.9906 - dense_20_acc: 0.9758 - dense_21_acc: 0.9969 - dense_22_acc: 0.9695 - dense_23_acc: 0.991 - ETA: 0s - loss: 0.3572 - dense_18_loss: 0.1352 - dense_19_loss: 0.0240 - dense_20_loss: 0.0714 - dense_21_loss: 0.0147 - dense_22_loss: 0.0830 - dense_23_loss: 0.0288 - dense_18_acc: 0.9515 - dense_19_acc: 0.9906 - dense_20_acc: 0.9758 - dense_21_acc: 0.9969 - dense_22_acc: 0.9695 - dense_23_acc: 0.991 - ETA: 0s - loss: 0.3571 - dense_18_loss: 0.1352 - dense_19_loss: 0.0240 - dense_20_loss: 0.0714 - dense_21_loss: 0.0147 - dense_22_loss: 0.0830 - dense_23_loss: 0.0288 - dense_18_acc: 0.9515 - dense_19_acc: 0.9906 - dense_20_acc: 0.9758 - dense_21_acc: 0.9969 - dense_22_acc: 0.9695 - dense_23_acc: 0.991 - ETA: 0s - loss: 0.3573 - dense_18_loss: 0.1352 - dense_19_loss: 0.0241 - dense_20_loss: 0.0715 - dense_21_loss: 0.0147 - dense_22_loss: 0.0830 - dense_23_loss: 0.0288 - dense_18_acc: 0.9515 - dense_19_acc: 0.9906 - dense_20_acc: 0.9758 - dense_21_acc: 0.9969 - dense_22_acc: 0.9695 - dense_23_acc: 0.991 - 773s 8ms/sample - loss: 0.3574 - dense_18_loss: 0.1353 - dense_19_loss: 0.0241 - dense_20_loss: 0.0715 - dense_21_loss: 0.0147 - dense_22_loss: 0.0831 - dense_23_loss: 0.0288 - dense_18_acc: 0.9515 - dense_19_acc: 0.9906 - dense_20_acc: 0.9758 - dense_21_acc: 0.9969 - dense_22_acc: 0.9695 - dense_23_acc: 0.9916 - val_loss: 0.3938 - val_dense_18_loss: 0.1467 - val_dense_19_loss: 0.0253 - val_dense_20_loss: 0.0809 - val_dense_21_loss: 0.0158 - val_dense_22_loss: 0.0909 - val_dense_23_loss: 0.0340 - val_dense_18_acc: 0.9483 - val_dense_19_acc: 0.9903 - val_dense_20_acc: 0.9729 - val_dense_21_acc: 0.9967 - val_dense_22_acc: 0.9671 - val_dense_23_acc: 0.9901 Epoch 5/5 864/102124 [..............................] - ETA: 11:36 - loss: 0.2492 - dense_18_loss: 0.0394 - dense_19_loss: 0.0604 - dense_20_loss: 0.0110 - dense_21_loss: 0.0981 - dense_22_loss: 0.0219 - dense_23_loss: 0.0185 - dense_18_acc: 1.0000 - dense_19_acc: 1.0000 - dense_20_acc: 1.0000 - dense_21_acc: 0.9688 - dense_22_acc: 1.0000 - dense_23_acc: 1.00 - ETA: 11:38 - loss: 0.2686 - dense_18_loss: 0.1238 - dense_19_loss: 0.0396 - dense_20_loss: 0.0127 - dense_21_loss: 0.0512 - dense_22_loss: 0.0264 - dense_23_loss: 0.0149 - dense_18_acc: 0.9531 - dense_19_acc: 1.0000 - dense_20_acc: 1.0000 - dense_21_acc: 0.9844 - dense_22_acc: 1.0000 - dense_23_acc: 1.00 - ETA: 11:41 - loss: 0.2683 - dense_18_loss: 0.0934 - dense_19_loss: 0.0412 - dense_20_loss: 0.0196 - dense_21_loss: 0.0355 - dense_22_loss: 0.0341 - dense_23_loss: 0.0446 - dense_18_acc: 0.9688 - dense_19_acc: 0.9896 - dense_20_acc: 0.9896 - dense_21_acc: 0.9896 - dense_22_acc: 0.9896 - dense_23_acc: 0.98 - ETA: 11:56 - loss: 0.2988 - dense_18_loss: 0.0925 - dense_19_loss: 0.0346 - dense_20_loss: 0.0610 - dense_21_loss: 0.0275 - dense_22_loss: 0.0468 - dense_23_loss: 0.0363 - dense_18_acc: 0.9688 - dense_19_acc: 0.9922 - dense_20_acc: 0.9688 - dense_21_acc: 0.9922 - dense_22_acc: 0.9844 - dense_23_acc: 0.99 - ETA: 11:46 - loss: 0.3317 - dense_18_loss: 0.0871 - dense_19_loss: 0.0288 - dense_20_loss: 0.0585 - dense_21_loss: 0.0225 - dense_22_loss: 0.0854 - dense_23_loss: 0.0495 - dense_18_acc: 0.9688 - dense_19_acc: 0.9937 - dense_20_acc: 0.9750 - dense_21_acc: 0.9937 - dense_22_acc: 0.9688 - dense_23_acc: 0.98 - ETA: 11:37 - loss: 0.2898 - dense_18_loss: 0.0776 - dense_19_loss: 0.0242 - dense_20_loss: 0.0520 - dense_21_loss: 0.0189 - dense_22_loss: 0.0756 - dense_23_loss: 0.0415 - dense_18_acc: 0.9740 - dense_19_acc: 0.9948 - dense_20_acc: 0.9792 - dense_21_acc: 0.9948 - dense_22_acc: 0.9740 - dense_23_acc: 0.98 - ETA: 11:32 - loss: 0.2893 - dense_18_loss: 0.0864 - dense_19_loss: 0.0211 - dense_20_loss: 0.0480 - dense_21_loss: 0.0288 - dense_22_loss: 0.0687 - dense_23_loss: 0.0362 - dense_18_acc: 0.9732 - dense_19_acc: 0.9955 - dense_20_acc: 0.9821 - dense_21_acc: 0.9911 - dense_22_acc: 0.9777 - dense_23_acc: 0.99 - ETA: 11:30 - loss: 0.2969 - dense_18_loss: 0.1038 - dense_19_loss: 0.0240 - dense_20_loss: 0.0463 - dense_21_loss: 0.0255 - dense_22_loss: 0.0646 - dense_23_loss: 0.0327 - dense_18_acc: 0.9648 - dense_19_acc: 0.9922 - dense_20_acc: 0.9844 - dense_21_acc: 0.9922 - dense_22_acc: 0.9805 - dense_23_acc: 0.99 - ETA: 11:28 - loss: 0.3507 - dense_18_loss: 0.1172 - dense_19_loss: 0.0218 - dense_20_loss: 0.0588 - dense_21_loss: 0.0474 - dense_22_loss: 0.0759 - dense_23_loss: 0.0296 - dense_18_acc: 0.9583 - dense_19_acc: 0.9931 - dense_20_acc: 0.9792 - dense_21_acc: 0.9896 - dense_22_acc: 0.9757 - dense_23_acc: 0.99 - ETA: 11:26 - loss: 0.3436 - dense_18_loss: 0.1110 - dense_19_loss: 0.0209 - dense_20_loss: 0.0611 - dense_21_loss: 0.0432 - dense_22_loss: 0.0797 - dense_23_loss: 0.0277 - dense_18_acc: 0.9625 - dense_19_acc: 0.9937 - dense_20_acc: 0.9781 - dense_21_acc: 0.9906 - dense_22_acc: 0.9719 - dense_23_acc: 0.99 - ETA: 11:24 - loss: 0.3205 - dense_18_loss: 0.1038 - dense_19_loss: 0.0198 - dense_20_loss: 0.0568 - dense_21_loss: 0.0394 - dense_22_loss: 0.0749 - dense_23_loss: 0.0258 - dense_18_acc: 0.9659 - dense_19_acc: 0.9943 - dense_20_acc: 0.9801 - dense_21_acc: 0.9915 - dense_22_acc: 0.9744 - dense_23_acc: 0.99 - ETA: 11:22 - loss: 0.3078 - dense_18_loss: 0.0999 - dense_19_loss: 0.0204 - dense_20_loss: 0.0543 - dense_21_loss: 0.0365 - dense_22_loss: 0.0721 - dense_23_loss: 0.0246 - dense_18_acc: 0.9661 - dense_19_acc: 0.9922 - dense_20_acc: 0.9818 - dense_21_acc: 0.9922 - dense_22_acc: 0.9740 - dense_23_acc: 0.99 - ETA: 11:22 - loss: 0.3316 - dense_18_loss: 0.1225 - dense_19_loss: 0.0210 - dense_20_loss: 0.0529 - dense_21_loss: 0.0402 - dense_22_loss: 0.0715 - dense_23_loss: 0.0236 - dense_18_acc: 0.9591 - dense_19_acc: 0.9904 - dense_20_acc: 0.9832 - dense_21_acc: 0.9904 - dense_22_acc: 0.9736 - dense_23_acc: 0.99 - ETA: 11:21 - loss: 0.3147 - dense_18_loss: 0.1179 - dense_19_loss: 0.0196 - dense_20_loss: 0.0503 - dense_21_loss: 0.0373 - dense_22_loss: 0.0676 - dense_23_loss: 0.0220 - dense_18_acc: 0.9598 - dense_19_acc: 0.9911 - dense_20_acc: 0.9844 - dense_21_acc: 0.9911 - dense_22_acc: 0.9754 - dense_23_acc: 0.99 - ETA: 11:21 - loss: 0.3029 - dense_18_loss: 0.1149 - dense_19_loss: 0.0183 - dense_20_loss: 0.0492 - dense_21_loss: 0.0349 - dense_22_loss: 0.0649 - dense_23_loss: 0.0207 - dense_18_acc: 0.9604 - dense_19_acc: 0.9917 - dense_20_acc: 0.9833 - dense_21_acc: 0.9917 - dense_22_acc: 0.9771 - dense_23_acc: 0.99 - ETA: 11:22 - loss: 0.3017 - dense_18_loss: 0.1094 - dense_19_loss: 0.0223 - dense_20_loss: 0.0477 - dense_21_loss: 0.0332 - dense_22_loss: 0.0686 - dense_23_loss: 0.0206 - dense_18_acc: 0.9629 - dense_19_acc: 0.9883 - dense_20_acc: 0.9844 - dense_21_acc: 0.9922 - dense_22_acc: 0.9746 - dense_23_acc: 0.99 - ETA: 11:21 - loss: 0.3005 - dense_18_loss: 0.1160 - dense_19_loss: 0.0211 - dense_20_loss: 0.0458 - dense_21_loss: 0.0313 - dense_22_loss: 0.0668 - dense_23_loss: 0.0195 - dense_18_acc: 0.9614 - dense_19_acc: 0.9890 - dense_20_acc: 0.9853 - dense_21_acc: 0.9926 - dense_22_acc: 0.9743 - dense_23_acc: 0.99 - ETA: 11:18 - loss: 0.2954 - dense_18_loss: 0.1125 - dense_19_loss: 0.0200 - dense_20_loss: 0.0473 - dense_21_loss: 0.0296 - dense_22_loss: 0.0675 - dense_23_loss: 0.0186 - dense_18_acc: 0.9635 - dense_19_acc: 0.9896 - dense_20_acc: 0.9826 - dense_21_acc: 0.9931 - dense_22_acc: 0.9722 - dense_23_acc: 0.99 - ETA: 11:19 - loss: 0.2951 - dense_18_loss: 0.1096 - dense_19_loss: 0.0194 - dense_20_loss: 0.0473 - dense_21_loss: 0.0283 - dense_22_loss: 0.0679 - dense_23_loss: 0.0225 - dense_18_acc: 0.9655 - dense_19_acc: 0.9901 - dense_20_acc: 0.9819 - dense_21_acc: 0.9934 - dense_22_acc: 0.9720 - dense_23_acc: 0.99 - ETA: 11:19 - loss: 0.3157 - dense_18_loss: 0.1153 - dense_19_loss: 0.0206 - dense_20_loss: 0.0514 - dense_21_loss: 0.0363 - dense_22_loss: 0.0702 - dense_23_loss: 0.0218 - dense_18_acc: 0.9609 - dense_19_acc: 0.9891 - dense_20_acc: 0.9797 - dense_21_acc: 0.9922 - dense_22_acc: 0.9719 - dense_23_acc: 0.99 - ETA: 11:20 - loss: 0.3242 - dense_18_loss: 0.1196 - dense_19_loss: 0.0197 - dense_20_loss: 0.0607 - dense_21_loss: 0.0346 - dense_22_loss: 0.0687 - dense_23_loss: 0.0209 - dense_18_acc: 0.9598 - dense_19_acc: 0.9896 - dense_20_acc: 0.9777 - dense_21_acc: 0.9926 - dense_22_acc: 0.9717 - dense_23_acc: 0.99 - ETA: 11:20 - loss: 0.3209 - dense_18_loss: 0.1155 - dense_19_loss: 0.0195 - dense_20_loss: 0.0593 - dense_21_loss: 0.0334 - dense_22_loss: 0.0720 - dense_23_loss: 0.0212 - dense_18_acc: 0.9616 - dense_19_acc: 0.9901 - dense_20_acc: 0.9787 - dense_21_acc: 0.9929 - dense_22_acc: 0.9688 - dense_23_acc: 0.99 - ETA: 11:19 - loss: 0.3223 - dense_18_loss: 0.1150 - dense_19_loss: 0.0193 - dense_20_loss: 0.0613 - dense_21_loss: 0.0321 - dense_22_loss: 0.0718 - dense_23_loss: 0.0227 - dense_18_acc: 0.9606 - dense_19_acc: 0.9905 - dense_20_acc: 0.9783 - dense_21_acc: 0.9932 - dense_22_acc: 0.9688 - dense_23_acc: 0.99 - ETA: 11:19 - loss: 0.3158 - dense_18_loss: 0.1119 - dense_19_loss: 0.0195 - dense_20_loss: 0.0602 - dense_21_loss: 0.0311 - dense_22_loss: 0.0708 - dense_23_loss: 0.0224 - dense_18_acc: 0.9622 - dense_19_acc: 0.9909 - dense_20_acc: 0.9792 - dense_21_acc: 0.9935 - dense_22_acc: 0.9701 - dense_23_acc: 0.99 - ETA: 11:19 - loss: 0.3292 - dense_18_loss: 0.1150 - dense_19_loss: 0.0198 - dense_20_loss: 0.0623 - dense_21_loss: 0.0325 - dense_22_loss: 0.0761 - dense_23_loss: 0.0236 - dense_18_acc: 0.9613 - dense_19_acc: 0.9912 - dense_20_acc: 0.9775 - dense_21_acc: 0.9925 - dense_22_acc: 0.9688 - dense_23_acc: 0.99 - ETA: 11:18 - loss: 0.3236 - dense_18_loss: 0.1113 - dense_19_loss: 0.0217 - dense_20_loss: 0.0602 - dense_21_loss: 0.0315 - dense_22_loss: 0.0738 - dense_23_loss: 0.0252 - dense_18_acc: 0.9627 - dense_19_acc: 0.9904 - dense_20_acc: 0.9784 - dense_21_acc: 0.9928 - dense_22_acc: 0.9700 - dense_23_acc: 0.99 - ETA: 11:18 - loss: 0.3237 - dense_18_loss: 0.1120 - dense_19_loss: 0.0209 - dense_20_loss: 0.0623 - dense_21_loss: 0.0304 - dense_22_loss: 0.0736 - dense_23_loss: 0.0244 - dense_18_acc: 0.9606 - dense_19_acc: 0.9907 - dense_20_acc: 0.9769 - dense_21_acc: 0.9931 - dense_22_acc: 0.9699 - dense_23_acc: 0.9931 1728/102124 [..............................] - ETA: 11:17 - loss: 0.3222 - dense_18_loss: 0.1149 - dense_19_loss: 0.0202 - dense_20_loss: 0.0609 - dense_21_loss: 0.0294 - dense_22_loss: 0.0733 - dense_23_loss: 0.0236 - dense_18_acc: 0.9609 - dense_19_acc: 0.9911 - dense_20_acc: 0.9777 - dense_21_acc: 0.9933 - dense_22_acc: 0.9699 - dense_23_acc: 0.99 - ETA: 11:16 - loss: 0.3278 - dense_18_loss: 0.1160 - dense_19_loss: 0.0215 - dense_20_loss: 0.0595 - dense_21_loss: 0.0285 - dense_22_loss: 0.0742 - dense_23_loss: 0.0281 - dense_18_acc: 0.9612 - dense_19_acc: 0.9903 - dense_20_acc: 0.9784 - dense_21_acc: 0.9935 - dense_22_acc: 0.9698 - dense_23_acc: 0.99 - ETA: 11:16 - loss: 0.3185 - dense_18_loss: 0.1132 - dense_19_loss: 0.0208 - dense_20_loss: 0.0578 - dense_21_loss: 0.0276 - dense_22_loss: 0.0719 - dense_23_loss: 0.0272 - dense_18_acc: 0.9625 - dense_19_acc: 0.9906 - dense_20_acc: 0.9792 - dense_21_acc: 0.9937 - dense_22_acc: 0.9708 - dense_23_acc: 0.99 - ETA: 11:17 - loss: 0.3139 - dense_18_loss: 0.1124 - dense_19_loss: 0.0202 - dense_20_loss: 0.0571 - dense_21_loss: 0.0267 - dense_22_loss: 0.0710 - dense_23_loss: 0.0264 - dense_18_acc: 0.9627 - dense_19_acc: 0.9909 - dense_20_acc: 0.9788 - dense_21_acc: 0.9940 - dense_22_acc: 0.9708 - dense_23_acc: 0.99 - ETA: 11:17 - loss: 0.3160 - dense_18_loss: 0.1102 - dense_19_loss: 0.0207 - dense_20_loss: 0.0573 - dense_21_loss: 0.0298 - dense_22_loss: 0.0707 - dense_23_loss: 0.0274 - dense_18_acc: 0.9639 - dense_19_acc: 0.9912 - dense_20_acc: 0.9785 - dense_21_acc: 0.9932 - dense_22_acc: 0.9707 - dense_23_acc: 0.99 - ETA: 11:16 - loss: 0.3228 - dense_18_loss: 0.1083 - dense_19_loss: 0.0238 - dense_20_loss: 0.0609 - dense_21_loss: 0.0322 - dense_22_loss: 0.0708 - dense_23_loss: 0.0269 - dense_18_acc: 0.9650 - dense_19_acc: 0.9896 - dense_20_acc: 0.9782 - dense_21_acc: 0.9924 - dense_22_acc: 0.9697 - dense_23_acc: 0.99 - ETA: 11:16 - loss: 0.3233 - dense_18_loss: 0.1091 - dense_19_loss: 0.0257 - dense_20_loss: 0.0615 - dense_21_loss: 0.0313 - dense_22_loss: 0.0695 - dense_23_loss: 0.0262 - dense_18_acc: 0.9642 - dense_19_acc: 0.9890 - dense_20_acc: 0.9779 - dense_21_acc: 0.9926 - dense_22_acc: 0.9706 - dense_23_acc: 0.99 - ETA: 11:16 - loss: 0.3161 - dense_18_loss: 0.1066 - dense_19_loss: 0.0250 - dense_20_loss: 0.0603 - dense_21_loss: 0.0304 - dense_22_loss: 0.0682 - dense_23_loss: 0.0255 - dense_18_acc: 0.9652 - dense_19_acc: 0.9893 - dense_20_acc: 0.9786 - dense_21_acc: 0.9929 - dense_22_acc: 0.9714 - dense_23_acc: 0.99 - ETA: 11:15 - loss: 0.3100 - dense_18_loss: 0.1049 - dense_19_loss: 0.0244 - dense_20_loss: 0.0593 - dense_21_loss: 0.0296 - dense_22_loss: 0.0669 - dense_23_loss: 0.0249 - dense_18_acc: 0.9661 - dense_19_acc: 0.9896 - dense_20_acc: 0.9792 - dense_21_acc: 0.9931 - dense_22_acc: 0.9722 - dense_23_acc: 0.99 - ETA: 11:15 - loss: 0.3050 - dense_18_loss: 0.1030 - dense_19_loss: 0.0245 - dense_20_loss: 0.0582 - dense_21_loss: 0.0290 - dense_22_loss: 0.0658 - dense_23_loss: 0.0246 - dense_18_acc: 0.9671 - dense_19_acc: 0.9899 - dense_20_acc: 0.9797 - dense_21_acc: 0.9932 - dense_22_acc: 0.9730 - dense_23_acc: 0.99 - ETA: 11:14 - loss: 0.3049 - dense_18_loss: 0.1030 - dense_19_loss: 0.0249 - dense_20_loss: 0.0593 - dense_21_loss: 0.0283 - dense_22_loss: 0.0653 - dense_23_loss: 0.0241 - dense_18_acc: 0.9671 - dense_19_acc: 0.9893 - dense_20_acc: 0.9794 - dense_21_acc: 0.9934 - dense_22_acc: 0.9729 - dense_23_acc: 0.99 - ETA: 11:13 - loss: 0.3035 - dense_18_loss: 0.1010 - dense_19_loss: 0.0246 - dense_20_loss: 0.0585 - dense_21_loss: 0.0303 - dense_22_loss: 0.0644 - dense_23_loss: 0.0247 - dense_18_acc: 0.9679 - dense_19_acc: 0.9896 - dense_20_acc: 0.9800 - dense_21_acc: 0.9928 - dense_22_acc: 0.9736 - dense_23_acc: 0.99 - ETA: 11:13 - loss: 0.3016 - dense_18_loss: 0.1022 - dense_19_loss: 0.0247 - dense_20_loss: 0.0573 - 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dense_19_acc: 0.9891 - dense_20_acc: 0.9818 - dense_21_acc: 0.9935 - dense_22_acc: 0.9738 - dense_23_acc: 0.99 - ETA: 11:11 - loss: 0.2967 - dense_18_loss: 0.1000 - dense_19_loss: 0.0254 - dense_20_loss: 0.0548 - dense_21_loss: 0.0274 - dense_22_loss: 0.0638 - dense_23_loss: 0.0252 - dense_18_acc: 0.9673 - dense_19_acc: 0.9886 - dense_20_acc: 0.9815 - dense_21_acc: 0.9936 - dense_22_acc: 0.9744 - dense_23_acc: 0.99 - ETA: 11:10 - loss: 0.2934 - dense_18_loss: 0.0991 - dense_19_loss: 0.0249 - dense_20_loss: 0.0538 - dense_21_loss: 0.0268 - dense_22_loss: 0.0642 - dense_23_loss: 0.0247 - dense_18_acc: 0.9674 - dense_19_acc: 0.9889 - dense_20_acc: 0.9819 - dense_21_acc: 0.9937 - dense_22_acc: 0.9743 - dense_23_acc: 0.99 - ETA: 11:10 - loss: 0.2901 - dense_18_loss: 0.0996 - dense_19_loss: 0.0243 - dense_20_loss: 0.0527 - dense_21_loss: 0.0262 - dense_22_loss: 0.0630 - dense_23_loss: 0.0242 - dense_18_acc: 0.9667 - dense_19_acc: 0.9891 - dense_20_acc: 0.9823 - dense_21_acc: 0.9939 - dense_22_acc: 0.9749 - 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dense_23_acc: 0.991 - ETA: 0s - loss: 0.3333 - dense_18_loss: 0.1262 - dense_19_loss: 0.0231 - dense_20_loss: 0.0653 - dense_21_loss: 0.0134 - dense_22_loss: 0.0781 - dense_23_loss: 0.0271 - dense_18_acc: 0.9541 - dense_19_acc: 0.9906 - dense_20_acc: 0.9775 - dense_21_acc: 0.9969 - dense_22_acc: 0.9708 - dense_23_acc: 0.991 - ETA: 0s - loss: 0.3333 - dense_18_loss: 0.1262 - dense_19_loss: 0.0231 - dense_20_loss: 0.0653 - dense_21_loss: 0.0135 - dense_22_loss: 0.0780 - dense_23_loss: 0.0271 - dense_18_acc: 0.9541 - dense_19_acc: 0.9906 - dense_20_acc: 0.9775 - dense_21_acc: 0.9969 - dense_22_acc: 0.9708 - dense_23_acc: 0.991 - ETA: 0s - loss: 0.3333 - dense_18_loss: 0.1262 - dense_19_loss: 0.0231 - dense_20_loss: 0.0653 - dense_21_loss: 0.0135 - dense_22_loss: 0.0780 - dense_23_loss: 0.0271 - dense_18_acc: 0.9541 - dense_19_acc: 0.9906 - dense_20_acc: 0.9775 - dense_21_acc: 0.9969 - dense_22_acc: 0.9708 - dense_23_acc: 0.991 - 769s 8ms/sample - loss: 0.3333 - dense_18_loss: 0.1261 - dense_19_loss: 0.0232 - dense_20_loss: 0.0653 - dense_21_loss: 0.0135 - dense_22_loss: 0.0780 - dense_23_loss: 0.0271 - dense_18_acc: 0.9541 - dense_19_acc: 0.9906 - dense_20_acc: 0.9775 - dense_21_acc: 0.9969 - dense_22_acc: 0.9708 - dense_23_acc: 0.9917 - val_loss: 0.3900 - val_dense_18_loss: 0.1453 - val_dense_19_loss: 0.0253 - val_dense_20_loss: 0.0807 - val_dense_21_loss: 0.0152 - val_dense_22_loss: 0.0908 - val_dense_23_loss: 0.0327 - val_dense_18_acc: 0.9490 - val_dense_19_acc: 0.9905 - val_dense_20_acc: 0.9733 - val_dense_21_acc: 0.9967 - val_dense_22_acc: 0.9674 - val_dense_23_acc: 0.9905
Test the model¶
We do the model evaluation and test the data in two ways.
- The evaluation is perform on the hold-out test set we seperated before
- And we also made a "fake" toxic comments to perform sanity check. (Sorry we don't mean to say that)
In [106]:
score = model.evaluate(x=X_test, y=[y1_test, y2_test, y3_test, y4_test, y5_test, y6_test], verbose=1)
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===================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 117s 4ms/sample - loss: 0.7071 - dense_18_loss: 0.1417 - dense_19_loss: 0.0273 - dense_20_loss: 0.0784 - dense_21_loss: 0.0105 - dense_22_loss: 0.0910 - dense_23_loss: 0.0333 - dense_18_acc: 0.9490 - dense_19_acc: 0.9904 - dense_20_acc: 0.9733 - dense_21_acc: 0.9977 - dense_22_acc: 0.9658 - dense_23_acc: 0.9911
In [107]:
# Forgive us! We just want to experiment!
comments = "Really stupid in this place. fuck off you troll!"
comments = pd.Series(comments)
comments_1 = preprocessing_test(comments, tokenizer)
In [108]:
pred = model.predict(comments_1)
result = []
labels = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]
for p in pred:
result.append(float(p[0]))
print(dict(zip(labels, result)))
{'toxic': 0.9196690917015076, 'severe_toxic': 0.025544999167323112, 'obscene': 0.7885405421257019, 'threat': 0.0068905968219041824, 'insult': 0.42700138688087463, 'identity_hate': 0.02542535960674286}
In [109]:
import h5py
model.save('ltsm_model.h5', save_format = 'tf')
In [110]:
from keras.utils import CustomObjectScope
from keras.initializers import glorot_uniform
from tensorflow.keras.models import load_model
with CustomObjectScope({'GlorotUniform': glorot_uniform()}):
new_model_2 = load_model('ltsm_model.h5')
In [111]:
new_model_2.predict(comments_1)
Out[111]:
[array([[0.9196691]], dtype=float32), array([[0.025545]], dtype=float32), array([[0.78854054]], dtype=float32), array([[0.0068906]], dtype=float32), array([[0.4270014]], dtype=float32), array([[0.02542536]], dtype=float32)]
In [114]:
# save tokenizer
import json
tokenizer_json = tokenizer.to_json()
with open('tokenizer.json', 'w', encoding='utf-8') as f:
f.write(json.dumps(tokenizer_json, ensure_ascii=False))
In [116]:
from keras_preprocessing.text import tokenizer_from_json
with open('tokenizer.json') as f:
data = json.load(f)
tokenizer = tokenizer_from_json(data)